Multi-time Scale Optimization Research Articles

Multi-timescale optimization of integrated energy system with diversified utilization of hydrogen energy under the coupling of green certificate and carbon trading

Promoting renewable energy and developing low-carbon integrated energy systems are noteworthy in the energy sector. However, in existing works on the integrated energy system, the coupling of green certificate and carbon trading mechanism under diversified utilization of hydrogen energy has not been fully considered to provide an incentive effect for uncertain renewable energy resources. Based on these considerations, a multi-timescale optimization of an integrated energy system with diversified utilization of hydrogen energy is studied under the coupling of green certificate and carbon trading mechanism. First, the coupled interaction mechanism of green certificate trading-stepped carbon trading is proposed by linking stepped carbon trading to green certificate trading through green certificates. Furthermore, the establishment of the diversified utilization unit of hydrogen energy addresses carbon emissions from the perspective of energy application and offers fresh concepts for a low-carbon economy. Then, an optimized scheduling model for multiple time scales of day-ahead/intraday/real-time is developed to reduce the impact of renewable energy prediction errors on system operation. Results acquired from case studies demonstrate the effectiveness in terms of achieving economic and low carbon. The accommodation level of renewable energy with a coupling mechanism improves by 3.08% compared to that without considering coupling. In addition, the diversified utilization of the hydrogen energy model enables the reduction in total cost and carbon emission by 5.14% and 2.4927 tonnes in comparison to the traditional power-to-gas mode.

Research on Multi-time Scale Integrated Energy Scheduling Optimization Considering Carbon Constraints

To address the challenge of grasping carbon emission costs in different dispatching cycles under multiple time scales of the Integrated Energy System (IES), a carbon-constrained multi-time scale optimization method for integrated energy systems is proposed. First, a novel mechanism was proposed, Carbon Constraint Mechanism, which includes carbon tax, green certificate trading, and tiered carbon trading mechanism. Second, the carbon constraint mechanism was introduced into multi time scale scheduling optimization, and an IES multi time scale low-carbon optimization scheduling model was established. Third, the economic cost, carbon emissions, and primary energy consumption were considered as objective functions in multi-objective optimal scheduling. Moreover, utilized day ahead scheduling results and real-time load forecasting to dynamically adjust the output power of IES equipment in combination with power change penalties and carbon constraints. The simulation results showed that, compared to dispatch optimization strategies that solely prioritized economy, this study reduced carbon emissions by 28.38% across multiple time scales while enhancing the economic performance of IES by considering carbon constraints. The multi-time scale dispatch optimization, incorporating carbon constraint mechanisms, improved the system's stability and low-carbon economy, proving the feasibility and effectiveness of the proposed optimal dispatch strategy.

A multi-timescale optimization method for integrated energy systems with carbon capture and accounting

As the goal of “double carbon”, integrated energy systems aiming at the development of low-carbon economy are developing rapidly, and carbon capture and other emission reduction technologies are gradually gaining more extensive development space. For controlling carbon emissions and enhance the consumption of renewable energy. This work proposes to introduce carbon capture technology in the framework of integrated energy system and optimize the energy dispatching of integrated energy system in multiple time scales, and design a multi-time scale optimization model of integrated energy system with carbon capture. Based on the basic architecture of a low-carbon integrated energy system, this study analyzes the power characteristics of each unit of the integrated energy system, which consists of thermal power units, gas turbines, electric boilers, batteries, gas storage, heat storage, etc. By studying the energy conversion and storage processes of each unit, a power model of each unit of the integrated energy system is established. On this basis, the relationship between carbon emissions and unit output of thermal power units and gas turbines was studied, and a carbon emission model for the energy supply unit in the comprehensive energy system was established. At the same time, in order to solve the problem of carbon emission reduction under the day ahead scheduling plan of the integrated energy system, considering the emission reduction goals and system operation security factors, the study analyzed the economic model and carbon emission model of the integrated energy system, established the day ahead low-carbon scheduling model of the integrated energy system, and reasonably planned the output of each unit that can achieve the carbon emission reduction goals on the premise of meeting the balance of supply and demand. The innovation of the research method of this paper is that this paper establishes a multi time scale rolling optimization model under the emission reduction goal of the integrated energy system. Based on the day ahead scheduling scheme obtained in the day ahead low-carbon scheduling phase, the day ahead plan is first revised through 4 h rolling scheduling in the day; Then, with the goal of minimizing the adjustment amount, fine tune the unit output within 15 minutes to provide a daily output plan for subsequent low-carbon emission reduction targets. The outcomes indicate that in the practical application, the carbon emission of the optimized model in the peak hour 11:00 to 12:00 phase is 118 tons, which is 7 tons less than the 125 tons of the traditional model. In summary, it demonstrates that the studied multi-timescale optimization model of integrated energy system with carbon capture has good application. We have studied and analyzed the low-carbon implementation mechanism of coordinated cooperation in multiple time scales, and constructed a multi time scale rolling optimization model, laying a theoretical foundation for subsequent low-carbon scheduling research. This enables the system to formulate more accurate and reasonable scheduling plans, while improving the low-carbon performance and economic benefits of the system, providing reference for the low-carbon development of the power system.

A Distributed Multi-Timescale Dispatch Strategy for a City-Integrated Energy System with Carbon Capture Power Plants

In city-integrated energy systems containing electric–thermal multi-energy sources, the uncertainty of renewable energy sources and the fluctuation of loads challenge the safe, efficient, economic and stable operation of the integrated energy systems. This paper introduces a novel approach for the operation of a carbon capture plant/CHP with PV accommodation within a city-integrated energy system. The proposed strategy aims to maximize the utilization of photovoltaic (PV) power generation and carbon capture equipment, addressing issues related to small-scale CHP climbing constraints and short-term output regulation. Additionally, this paper presents a multi-timescale optimal scheduling strategy, which effectively addresses deviations caused by PV fluctuations and load changes. This was achieved through a detailed analysis of the CHP climbing constraints, carbon capture equipment operation and real-time characteristics of PV power generation. This paper introduces a fully distributed neural dynamics-based optimization algorithm designed to address multi-timescale optimization challenges. Utilizing rolling cycles, this algorithm computes both day-ahead and real-time scheduling outcomes for urban integrated energy systems. Theoretical analyses and numerical simulations were conducted to validate the precision and efficacy of the proposed model and algorithm. These analyses quantitatively evaluate the scheduling performance of PV power generation and carbon capture CHP systems across various timescales.

Multi-time scale optimization study of integrated energy system considering dynamic energy hub and dual demand response

The integrated energy system (IES) is one of the current hot topics in energy research field. However, simplifying models often assumes constant energy conversion device efficiency during optimized operation, which leads to impractical operating modes for the IES. Additionally, the outcomes of device outputs vary across different time scales. The inaccuracy and temporal dispersion of energy supply are becoming more and more prominent, and the modeling and optimal scheduling of the IES are facing great challenges. To effectively address these issues, a multi-timescale optimal scheduling strategy for integrated energy systems was constructed, which considers the variable operating conditions of the equipment. First, a dynamic energy hub model with iterative correction between the equipment efficiency and load rate was established. Subsequently, on this basis, a multi-time scale optimization model of day-ahead, intraday, and real-time dispatch was constructed. Further, a day-ahead and intra-day demand response strategy was introduced to encourage customers to actively participate in demand-side management. Finally, a case study based on the energy system in the Xiongan area of China was conducted. From the optimization results, it was demonstrated that the proposed model in this work can solve the problem of inaccurate equipment efficiency in the fixed condition model and significantly improve the accuracy of system operation and dispatch. The implementation of a multi-time scale optimization and regulation strategy for IES can reduce the cost by 5.82%. Combined also with the dual demand response strategy, the total costs can be reduced by up to 10.07%.

Multi-Time Scale Optimization of Urban Micro-Grids Considering High-Penetration of PVs and Heterogeneous Energy Storage Systems

Multi-Time Scale Optimization of Urban Micro-Grids Considering High-Penetration of PVs and Heterogeneous Energy Storage Systems

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

Multi-timescale optimization scheduling of regional integrated energy system based on source-load joint forecasting

The uncertainty of renewable energy output randomness and multiple load demand uncertainty significantly increases the complexity of optimization and scheduling in regional integrated energy systems (RIES), rendering traditional optimization methods insufficient. This paper proposes a multi-scale optimization and scheduling strategy for RIES based on source-load forecasting. Firstly, considering the coupling characteristics of sources and loads, we construct a Multi-Task Multi-Head-based Source-Load Joint Forecasting Model (MTMH-MTL), which provides effective predictive data for optimization and scheduling. Secondly, to further mitigate the impacts of source and load randomness, we develop a multi-time scale optimization model, which plans unit output in two stages, namely, day-ahead and intra-day, through a rolling mechanism. Finally, case simulations confirm that the proposed approach flexibly reduces the influence of randomness on system operations. Compared to traditional methods, our approach reduces economic indicators by 9.17%, environmental indicators by 5.66%, significantly enhances RIES energy utilization, ensures dynamic user demand fulfillment, and strengthens energy stability.

Digital twin based multi-objective energy management strategy for energy internet

Energy management problem (EMP) has been a widely researched topic in optimal operation of Energy Internet (EI). However, the rapid growth in energy network scale and penetration of distributed renewable generations (DRGs) bring new challenges to energy management. Therefore, a digital twin (DT) based parallel energy management strategy is proposed for the large-scale EI which consists of We-energy (WE). Firstly, a parallel energy management framework is proposed. By establishing this triple parallel structure, states of energy networks can be observed realtimely, which enables flexible responses to fluctuations of DRGs and energy plug-and-play. Abandoned renewable energy is taken into account in the optimization model, which promotes the utilization of renewable energy. Then, a multi-timescale optimization strategy is proposed to handle different timescales of multi-energy networks. Furthermore, for better obtaining and processing information and avoiding dimensional curse, a DT based deep Q-learning algorithm (DQN) is proposed. Eventually, compared with the traditional benefit consensus based strategy, the simulation verifies the effectiveness of the DT based parallel energy management strategy.

Multi-time Scale Coordinated Optimization Scheduling Considering Flexible Resources

With the growth of new energy installations such as wind power and photovoltaic, the contradiction between supply and demand balance in the power system is becoming increasingly prominent, mainly reflected in the abandonment of new energy, deep peak shaving of units, and Primary Frequency Regulation of units. This article proposes a multi-time scale optimization scheduling strategy that considers multi-link flexible resources. It aims to promote the consumption of clean energy, enhance the flexibility of the power system, and promote the coordinated operation of different time scales. Firstly, the operational characteristics of the power system at different time scales were analyzed, and the current situation and problems of deep peak shaving and Primary Frequency Regulation were analyzed. Secondly, an optimization model was established with the goal of minimizing system operating costs and new energy waste electricity, which includes three stages: weekly optimization, daily optimization, and real-time optimization. Finally, an IEEE-30 node network structure was used for example verification, and the results showed that the proposed scheduling mode could effectively improve system flexibility.

Two-timescale coordinated operation of wind-advanced adiabatic compressed air energy storage system: A bilevel stochastic dynamic programming method

Renewable energy is a promising solution to address the energy crisis and environmental issues, but it comes with challenges due to its inherent volatility and limited dispatchability. Advanced adiabatic compressed air energy storage (AA-CAES) is a favorable partner for centralized renewable integration, due to its numerous benefits, such as large capacity, long lifetime, fast response capability, and zero carbon emissions. For the economic management of a wind-AACAES system, a battery-like AA-CAES dispatch model is proposed, where the charging/discharging efficiencies and capacities are dependent on the operating power and state-of-charge (SoC) to capture the part-load characteristics. A hybrid control strategy is proposed to enhance the flexibility and efficiency of the compression side under off-design conditions caused by simultaneous changes in back pressure and load. To provide dispatch and control strategies of AA-CAES, a multi-timescale optimization problem is established. The slow timescale determines the baseline output scheduling on an hourly basis to maximize total revenue, while the fast timescale updates the real-time adjustment every five minutes to minimize the penalty of tracking error. A bi-level stochastic dynamic programming (SDP) framework is formulated to incorporate the wind uncertainties and multi timescale coordination, where terminal SoCs and value functions are essential to integrate decisions across slow and fast timescales. A non-iterative parametric programming-based method is proposed to solve the slow-timescale SDP, and a simulation-based rollout algorithm is applied to extract the fast-timescale actions, which yields evident improvement over any heuristic base policy that is sequentially consistent. The numerical results demonstrate that the proposed method has a performance gap of approximately 7.1%, which outperforms the rule-based method by 32.6%. The installation of AA-CAES improves the total revenue by 11.9%. Additionally, the hybrid control strategy enhances the charging flexibility and efficiency by 30.7% and 4.3%, respectively, resulting in a 3.9% reduction in tracking deviation penalty.

Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system

With the increasing uncertainty of energy supply side output, fully encouraging users to participate in demand response through different types of demand response incentive mechanisms has become one of the effective ways to deal with the uncertainty of integrated energy system operation and improve the overall energy efficiency. However, in existing studies, the coordination of uncertainty handling, optimization of demand response incentive strategies, and demand response measures at different time scales have not been adequately considered in the operation of integrated energy systems. Based on these considerations, this paper proposes a multi time-scale game optimization scheduling model for Park-level Integrated Energy System considering multiple types of demand response models. In the day-ahead stage, a Park-level Integrated Energy System optimization game scheduling model based on the demand response comprehensive incentive mechanism is established, and the uncertainty of the predicted value of distributed renewable energy and multi-type energy load was characterized based on the fuzzy chance-constrained programming method. In the intraday and real-time stages, a rolling optimization scheduling model is established with the minimum cost of Park-level Integrated Energy System operator scheduling. For the proposed model, an improved particle swarm optimization algorithm and an iterative solution strategy of CPLEX solver are introduced. Finally, the simulation results of an actual case show that the proposed model can effectively improve the Park-level Integrated Energy System operator and user economy while ensuring reliability.

Multi-Timescale Optimal Operation Strategy for Renewable Energy Power Systems Based on Inertia Evaluation

To enhance the operational dependability of renewable energy power systems with high proportions, this study proposes a multi-timescale optimization strategy based on the inertia evaluation model. Firstly, the inertia evaluation model is established based on the factors influencing the inertia demand of the power system, and the concept of the inertia margin coefficient is introduced. Secondly, to address the uncertainties associated with sustainable energy output and the cost of carbon emissions, a multi-timescale optimization operation model is formulated for day-ahead, intraday, and real-time operations, aimed at economic optimization. The output status of each unit is obtained and adjusted in a timely manner in the next stage, while meeting the system’s inertia demand, to derive the final scheduling strategy. Lastly, a sensitivity analysis of the inertia margin coefficient is conducted through simulations to validate the effectiveness and cost-efficiency of the proposed scheduling strategy.

Multi-time scale optimization scheduling of microgrid considering source and load uncertainty

With the increase of renewable energy penetration in microgrid and the stochasticity of customer load, microgrid faces new difficulties in maintaining the smooth power of contact lines and system economy when achieving optimal scheduling. In order to balance the accuracy, economy and robustness of microgrid scheduling better, a multi-time scale optimal scheduling strategy for microgrids considering the uncertainty of source and load is proposed. In the day-ahead scheduling stage, a two-stage distributionally robust optimal scheduling model is established with the objective of minimizing the comprehensive day-ahead scheduling cost of the microgrid, and the optimal day-ahead scheduling solution is found under the probability distribution of the worst scenario. In the intra-day scheduling stage, based on the prediction value of source and load with higher accuracy, a distributed model predictive control method is used to build an intra-day rolling optimization and real-time adjustment optimal scheduling model, which ensures the effective implementation of the day-ahead plan and fully suppresses power fluctuation of the contact line. The simulation results show that the proposed multi-time scale optimal scheduling method can not only maintain the smooth power of contact lines, but also achieve robust and economic operation of the microgrid.

Approach to Multi-Timescale Optimization for Distributed Energy Resources Clusters Considering Flexibility Margin

The disordered access of massively distributed energy resources (DERs) brings great challenges to the operation stability of the power grid. This paper puts forward the concept of a cluster, which gathers DERs in large quantities, small capacities, dispersion and disorder to form a large, centralized and orderly whole, namely cluster, with certain incentive measures. In this paper, a multi-timescale optimization method of day-ahead planning and intra-day rolling optimization is proposed according to the characteristics of aggregated clusters and the requirements of China’s power grid architecture. Specifically, the day ahead model is proposed in two steps: the first step is to establish an optimization model with the goal of optimal fitting the target load curve and maximizing the utilization of DERs; The second step is to establish a potential game model considering the reasonable distribution of cluster benefits. Taking the minimum percentage of output correction of each cluster as the objective, considering the deviation of load forecasting and the deviation of day ahead instruction execution, an intra-day rolling optimization model is established. Finally, the application scenario of cluster participation in power grid auxiliary peak shaving is simulated and verified. The simulation results show that the cluster collaborative optimization method proposed in this paper can effectively reduce the load peak valley difference and maximize the use of cluster resources. The optimization tasks can be reasonably allocated while ensuring the stable and reliable operation of the power grid.

Bi-Level Coordinated Operation Strategy of Multiple Microgrids in Multi-Time Scale

Microgrid (MG) has a significant effect on achieving local consumption of distributed renewable energy, power supply in remote areas, and so on. With the increasing number of MGs, the operation problem of multiple MGs has been more and more researched, while the coordinated operation and the bidding strategy are still the main problems limiting the development of multiple MGs. In this article, a bi-level optimization operation model of multiple MGs is studied. First, the multi-level control structure based on the multi-agent system and the multi-time scale optimization strategy framework of multiple MGs are constructed. The multi-level control structure is divided into three layers, including the device layer, MG layer, and power market layer. Second, the multi-time scale optimized operation model is established, including day-ahead model, intraday model, and real-time model. In addition, a game theory model is proposed to improve the interests or reduce costs of MGs. Finally, a case including three MGs is simulated and analyzed. The results show that the proposed multi-level structure of multiple MGs and multi-time scale optimization strategies are effective.

Multi time scale operation optimization of EHHGS considering equipment uncertainty and response characteristics

The optimal operation of electric hydrogen coupling systems has gradually become a key issue because of the considerable attention being paid to hydrogen energy. Therefore, this study investigates the multi-time scale operation optimization of a rural electricity hydrogen natural gas coupling system（EHHGS）considering equipment uncertainty and response characteristics. First, the framework of an EHHGS in rural areas is designed, and the equipment in the framework is modeled finely. Furthermore, the uncertainty and response characteristics of each link in the EHHGS are analyzed from the source, coupling, and load ends. Thereafter, the multi-time scale operation optimization model of the EHHGS is constructed based on the time resolution optimization model of operation optimization. Finally, a numerical example is analyzed to verify the effectiveness of the uncertainty model of each link and the multi-time scale optimization model.

Study on the multitime scale rolling optimization operation of a near-zero energy building energy supply system

The combined cooling, heating and power system of near-zero energy buildings can provide cold energy, heat energy and electricity to users at the same time, realizing combined cold, heat and power generation. However, the randomness of renewable energy output and the volatility of user loads lead to an imbalance between energy supply and demand. Therefore, a three-stage multitime scale optimization operation method is proposed. This method establishes a three-stage optimization model of ‘day-ahead, day-in rolling, real-time feedback’ to optimize the output of the unit step by step. In the day-ahead phase, the optimization goal is to take the lowest daily operating cost while considering the environmental cost. In the day-in rolling stage, the optimization goal is to control the minimum cost of energy purchase in the time domain and the minimum penalty cost of unit output change. The real-time feedback phase optimizes with the minimum total device power adjustment. The results show that the cost saving rates of multitime scale optimization strategies on typical winter days, typical transition season days and typical summer days are 0.90%, 2.32% and 2.12%, respectively. Compared with the ideal cost of different typical days, the cost deviation rates are 0.21%, 0.51% and 1.97%, respectively. Compared with the optimization of the previous day, the total power of the grid connection line on different typical days is −8.0%, −10.65% and −6.23%, respectively. Therefore, this novel optimization method has good economy, stability and accuracy and has a guiding effect on the formulation of each equipment operation plan.

Robust optimization of the active distribution network involving risk assessment

Inherent dynamic constraints of distributed generations (DGs) and the correlation between injected variables bring great challenges to distribution network operation. In order to improve the degree of coupling and interconnection coordination between different energy devices, improve the ability of the distribution network to cope with the uncertainty of DGs, achieve low-carbon operation, and improve the environmental friendliness of distribution network operation, this article proposes a robust optimization approach involving risk assessment. The semi-invariant method and scene clustering are used to deal with the uncertainty of DGs and load, thus formulating a robust optimization model for distribution network distribution based on risk indices. To address the time-varying constraints of energy storage systems (ESSs) and gas turbines, a two-stage box-based decomposition model is established. Dynamic constraints are included in the first stage to constrain the operating state and operating domain of the unit and ESSs. In the second stage, the multi-timescale optimization problem is transformed into multiple single-timescale optimization problems, which are solved by the column and constraint generation (C&CG) algorithm to improve the solution efficiency. The feasibility of the comprehensive optimization model based on dynamic reconfiguration and distributed robust optimization (DRO) is demonstrated with the PG&E 69 bus system.

Two-stage optimization of a virtual power plant incorporating with demand response and energy complementation

The virtual power plant (VPP) breaks the geographical restrictions of environment and resource types by virtue of aggregating the distributed energy resources and dispatchable loads. In view of the impact of large-scale energy consumption by users on the operational safety of VPP, based on the existing demand response (DR) researches, this paper adds a penalty mechanism for the reasonable management of loads. For the difference of resources in multiple VPPs, the current researches ignore the energy interaction between different VPPs, and the external operator is introduced innovatively in this paper to coordinate the real-time complementation of regional energy. Considering the uncertainty from renewable energy and load, a two-stage scheduling strategy for VPP with multi-time scale optimization is proposed, including a long-time scale day-ahead scheduling and a short-time scale real-time scheduling. On the basis of DR and unit constraints, the resource scheduling plan is formulated in the day-ahead stage. In the real-time stage, the energy complementation between VPPs is considered under the framework of rolling optimization for the correction of the previous plan. The scheduling of both stages is aimed at the economic operation of the VPP. Simulation results show that the DR flexibly optimizes the load distribution while bringing economic benefits, and the total profit can be increased by up to 6.65%. Moreover, the results show that the energy interaction under the external operator coordination significantly reduces the total correction cost by up to 8.23%, and the correction cost is decreased with the extension of the rolling optimization scheduling period.