Intra-day Scheduling Research Articles

Two-Stage Optimization Scheduling of Integrated Energy Systems Considering Demand Side Response

This study proposes a two-level optimization scheduling method for multi-region integrated energy systems (IESs) that considers dynamic time intervals within the day, addressing the diverse energy characteristics of electricity, heat, and cooling. The day-ahead scheduling aims to minimize daily operating costs by optimally regulating controllable elements. For intra-day scheduling, a predictive control-based dynamic rolling optimization model is utilized, with the upper-level model handling slower thermal energy fluctuations and the lower-level model managing faster electrical energy fluctuations. Building on the day-ahead plan, different time intervals are used for fast and slow layers. The slow layer establishes a decision index for command cycle intervals, dynamically adjusting based on ultra-short-term forecasts and incremental balance corrections. Case studies demonstrate that this method effectively leverages energy network characteristics, optimizes scheduling intervals, reduces adjustment costs, and enhances system performance, achieving coordinated operation of the IES network and multi-energy equipment.

Two-Stage Distributed Robust Optimization Scheduling Considering Demand Response and Direct Purchase of Electricity by Large Consumers

The integration of large-scale wind power into power systems has exacerbated the challenges associated with peak load regulation. Concurrently, the ongoing advancement of electricity marketization reforms highlights the need to assess the impact of direct electricity procurement by large consumers on enhancing the flexibility of power systems. In this context, this paper introduces a Distributed Robust Optimal Scheduling (DROS) model, which addresses the uncertainties of wind power generation and direct electricity purchases by large consumers. Firstly, to mitigate the effects of wind power uncertainty on the power system, a first-order Markov chain model with interval characteristics is introduced. This approach effectively captures the temporal and variability aspects of wind power prediction errors. Secondly, building upon the day-ahead scenarios generated by the Markov chain, the model then formulates a data-driven optimization framework that spans from day-ahead to intra-day scheduling. In the day-ahead phase, the model leverages the price elasticity of the demand matrix to guide consumer behavior, with the primary objective of maximizing the total revenue of the wind farm. A robust scheduling strategy is developed, yielding an hourly scheduling plan for the day-ahead phase. This plan dynamically adjusts tariffs in the intra-day phase based on deviations in wind power output, thereby encouraging flexible user responses to the inherent uncertainty in wind power generation. Ultimately, the efficacy of the proposed DROS method is validated through extensive numerical simulations, demonstrating its potential to enhance the robustness and flexibility of power systems in the presence of significant wind power integration and market-driven direct electricity purchases.

Optimal Scheduling of the Active Distribution Network with Microgrids Considering Multi-Timescale Source-Load Forecasting

Integrating distributed generations (DGs) into distribution networks poses a challenge for active distribution networks (ADNs) when managing distributed resources for optimal scheduling. To address this issue, this paper proposes a day-ahead and intra-day scheduling approach based on a multi-microgrid system. It starts with a CNN-LSTM-based generation and load forecasting model to address the impact of generation and load uncertainties on the power grid scheduling. Then, an optimal day-ahead and intra-day scheduling framework for ADN and microgrids is introduced using predicted generation and load information. The day-ahead scheduling is responsible for optimizing the power interactions between ADN and the connected microgrids, while intra-day scheduling focuses on minimizing the operational costs of microgrids. The effectiveness of the proposed scheduling strategy is verified via case studies performed on a modified IEEE 33-node ADN. The results show that the network loss of ADN and the operation costs of microgrids are reduced by 17.31% and 32.81% after the microgrid is integrated into the ADN. The peak-valley difference in microgrids decreased by 13.12%. The simulation shows a significant reduction in operational costs and load fluctuations after implementing the proposed day-ahead and intra-day scheduling strategy. The seamless coordination between the day-ahead scheduling and intra-day scheduling allows for the precise adjustment of transfer power, alleviating peak load demand and minimizing network losses in the ADN system.

Two-Stage Optimal Scheduling for Urban Snow-Shaped Distribution Network Based on Coordination of Source-Network-Load-Storage

With the widespread integration of distributed resources, optimizing the operation of urban distribution networks faces challenges including uneven source-load-storage distribution, fluctuating feeder power flows, load imbalances, and network congestion. The urban snow-shaped distribution network (SDN), characterized by numerous intra-station and inter-station tie switches, serves as a robust framework to intelligently address these issues. This study focuses on enhancing the safe and efficient operation of SDNs through a two-phase optimal scheduling model that coordinates source-network-load-storage. In the day-ahead scheduling phase, an optimization model is formulated to minimize operational costs and mitigate load imbalances. This model integrates network reconfiguration, energy storage systems (ESSs), and flexible load (FL). During intra-day scheduling, a rolling optimization model based on model predictive control adjusts operations using the day-ahead plan to minimize the costs and penalties associated with power adjustments. It provides precise control over ESS and FL outputs, promptly correcting deviations caused by prediction errors. Finally, the proposed model is verified by an actual example of a snow-shaped distribution network in Tianjin. The results indicate significant improvements in leveraging coordinated interactions among source-network-load-storage, effectively reducing spatial-temporal load imbalances within feeder clusters and minimizing the impact of prediction inaccuracies.

MPC-based robust optimization of smart apartment building considering uncertainty for conservative reduction

Undergoing a significant transition is demand-side management (DSM), brought about by changes in the energy landscape, including the extensive deployment of renewable energy sources (RES). One of the most important challenges in DSM is dealing with uncertainty. Robust optimization methods are widely known as an approach to deal with uncertainty. However, this method tends to be conservative in its solution because it considers worst-case scenarios. In this study, we develop a robust approach of model predictive control (MPC) to reduce conservativeness and maintain robustness in DSM of smart apartment building (SAB). Consisting of two stages is the robust optimization strategy employed in this study. The first stage involves day-ahead scheduling, where four different optimization approaches are implemented. In the second stage, intra-day scheduling is implemented on the basis of day-ahead operational plan. The application of this technique achieves reduced conservativeness and maintains robustness. The simulation results show that by using the proposed approach, compared to conventional robust optimization, while maintaining robustness, the operation cost of day-ahead scheduling can be reduced by 10.79% and the operation cost of intra-day scheduling can be reduced by 15%.

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

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

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.

Two-stage day-ahead and intra-day scheduling considering electric arc furnace control and wind power modal decomposition

As the uncertainty in energy supply increases, engaging various flexible resources in power systems has emerged as an effective strategy to address wind curtailment issues. Existing research insufficiently explores how EAFs can participate in reducing wind curtailment or optimizing flexible power system resources to decrease CO2 emissions from TTPs and enhance wind energy absorption across various timescales. This study introduces a dual timescale, dual-tier scheduling methodology incorporating EAF regulation and wind power modal decomposition. The day-ahead model integrates EAF demand response to decrease wind curtailment, a comprehensive wind power allocation, and a TTP carbon minimization model. The intra-day model employs wind power modal decomposition for optimizing BESSs within WFs and schedules TTPs to minimize CO2 emissions. Implemented through iterative genetic algorithms and CPLEX solver techniques, simulation results from a real-case scenario indicate that incorporating EAF loads reduces wind curtailment by 40.49 % and cuts CO2 emissions by 2.5 % in the day-ahead phase. Furthermore, by applying modal decomposition, TTPs and BESSs absorb fluctuating wind power components, ensuring maximal wind utilization and substantial CO2 reduction at TTPs. This approach offers vast potential to enhance power system flexibility, advance energy-intensive industries' transition, and foster low-carbon initiatives at TTPs.

Multi-Time-Scale Low-Carbon Economic Dispatch Method for Virtual Power Plants Considering Pumped Storage Coordination

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system.

Multi-time scale economic regulation model of virtual power plant considering multiple uncertainties of source, load and storag

A novel multi-stage time scale economic dispatch scheme is proposed for virtual power plants, taking into account the uncertainties arising from the connection of distribution network sources. This research introduces specific scheduling schemes tailored to various time scales within distribution networks, including a fuzzy optimized day ahead scheduling scheme, an intra-day scheduling scheme combined with Deep Q Network, and an adaptive optimized real-time scheduling scheme. This plan mainly considers the impact of photovoltaic output and conducts scheduling one day in advance through fuzzy optimization. In the intraday scheduling, different strategies were adopted in the study. By combining with Deep Q Network, research on scheduling for intraday demand within the power system. The analysis is conducted through rigorous modeling. Experimental tests were conducted to evaluate the performance of the proposed schemes. The day ahead dispatching primarily considers the impact of photovoltaic output and calculates the cost associated with each link in the grid under three different meteorological conditions. In the intra-day scheduling, the total costs for Scenario 1, Scenario 2, and Scenario 3 are found to be 34,724.5 yuan, 36,296.5 yuan, and 33,275.8 yuan, respectively. Notably, strategies 1 and 2 demonstrate lower costs compared to the pre-day scheduling, with the exception of Scenario 3. In real-time scheduling, considering the matching between sources and sources, the matching rate between sources and sources can be maintained at over 95%, and the stability and cost of the power grid have significantly decreased. In summary, by proposing a multi-stage time scale economic scheduling scheme, this study fully considers the uncertainty of the power supply of the distribution network access, as well as the different needs of day, day and real-time scheduling, providing an effective solution for the power dispatching of virtual power plants and providing important technical support for the reliability and economy of the power system.

A new integrated energy system cluster energy sharing framework adapted to high altitude areas

Since a renewable energy is connected to a high-altitude integrated energy system (HAIES), challenges arise for system operation. Shared energy storage as a jointly operated energy hub for multi-integrated energy system (IES) can effectively improve the economy and flexibility of the system. This paper proposes a joint day-ahead and intra-day scheduling strategy for a HAIES considering a shared composite energy storage operator (SCESO) and profit clearing scheme. First, a HAIES structure suitable for residential, industrial, and commercial applications is built based on combined oxygen supply and integrated demand response (IDR). An SCESO is then proposed to provide multiple energy sharing services for HAIES cluster, including electricity–oxygen–hydrogen. Accordingly, HAIESs and SCESOs are aggregated to create a HAIES cluster energy sharing framework. Second, during the intra-day scheduling stage, which is based on real-time forecast data that considers multiple source–load–charge uncertainties, the flexible adjustment capabilities of each unit of the framework are used to achieve intra-day energy balance and ensure the safe operation of the framework. Furthermore, a profit clearing scheme is proposed, introducing different energy contribution quantification methods for different energy cooperation modes. Finally, day-ahead and intra-day simulation results under multiple scenarios are analyzed. The simulation results demonstrate that the framework is optimal in terms of economy, adaptability to different RE outputs and RE uncertainties, and energy storage utilization efficiency.

Collaborative Optimization Scheduling of Multi-Microgrids Incorporating Hydrogen-Doped Natural Gas and P2G–CCS Coupling under Carbon Trading and Carbon Emission Constraints

In the context of “dual carbon”, restrictions on carbon emissions have attracted widespread attention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids, we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading, a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly, a multi-energy microgrid model is developed, coupled with hydrogen-doped natural gas system and P2G–CCS, and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this, a model for multi-microgrid electricity cooperation is established. Secondly, design optimization strategies for solving the model are divided into the day-ahead stage and the intraday stage. In the day-ahead stage, an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage, based on the day-ahead scheduling results, an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted, which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. Setting up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction.

Low-Carbon Economy Optimization of Integrated Energy System Considering Electric Vehicles Charging Mode and Multi-Energy Coupling

To realize the multi-energy coupling integrated energy system to meet the multi-class load demand and achieve the goal of low-carbon economic operation, a bi-level optimal configuration method for low-carbon economic operation of multi-energy coupling integrated energy system under different charging modes of electric vehicles is proposed. Firstly, an integrated energy system including cooling-heating-power-gas coupling is established. Then, within the level of intraday scheduling, factors such as ladder carbon trading mechanisms and different charging modes of electric vehicles are considered to achieve the lowest daily scheduling cost. In the configuration planning level, based on the daily operation cost, the equipment capacity is configured with the lowest equipment investment and annual operation costs. The bi-level model is solved by CPLEX solver and the optimal configuration scheme and scheduling results are obtained by mutual iteration. Finally, the impact of different charging methods of electric vehicles on the system economy and low-carbon performance is demonstrated through simulation examples. The results show that the charging method of electric vehicles and the carbon trading mechanism in this paper can significantly reduce carbon emissions and improve the system's operating cost. The proposed configuration approach can well achieve low-carbon economic operation of the multi-energy coupling integrated energy system.

Distributionally robust decarbonizing scheduling considering data-driven ambiguity sets for multi-temporal multi-energy microgrid operation

As concerns about environmental sustainability continue to grow, the demand for effective low-carbon energy management becomes increasingly pressing. This study presents a novel framework for multi-temporal multi-energy microgrids (MMGs), integrating advanced low-carbon technologies to meet this imperative. The framework ensures flexible operations to navigate uncertainties stemming from renewable energy sources (RES) and fluctuating energy demand. Facilitating multi-energy transactions, encompassing gas and power exchanges in both markets, the model accommodates uncertainties from RES and demand fluctuations. Objectives include reducing carbon emissions and improving economic efficiency. To address uncertainties in the MMG system, a data-driven distributionally robust optimization (DRO) method is employed. Day-ahead scheduling utilizes a two-stage three-level approach, deploying the column-and-constraints generation (C&CG) algorithm, showcasing the efficiency of DRO in minimizing energy waste and carbon emissions while remaining cost-effective. Practicality is demonstrated through real-time intra-day scheduling using the model predictive control (MPC) algorithm, building upon hourly day-ahead results. The effectiveness of both strategies is evaluated using empirical data from an MMG based on the IEEE 33-bus test system. This cost-saving framework not only achieves a significant carbon reduction of 10.6 % but also provides reliable and adaptable solutions, effectively addressing real-world variations in renewable energy and mitigating potential risks.

Microgrid energy management strategy using deep learning neural network

Under the background of a large number of uncertain new energy connected to microgrids, traditional model driven methods could be hard to address the speed problem of microgrid optimization. Therefore, we come up with a data-model hybrid driven microgrid scheduling strategy. This strategy includes three stages: day-ahead dispatching, preparing for day-in dispatching and day-in dispatching. In the day-ahead dispatching stage, basing on the day-ahead forecast data of renewable energy, the mixed integer linear programming (MILP) is used to solve the day-ahead dispatching plan. In the preparing for intraday scheduling stage, a neural network is introduced as the day scheduling model, new energy power and load power regard as inputs to the neural network, controllable units in the microgrid as outputs to train the neural network. During the intraday scheduling phase, neural networks are used to optimize the scheduling of controllable units in microgrids based on ultra short term predictions and pre day predictions. Finally, the effectiveness of the raised method was verified through numerical analysis.

Multi-time Optimal Dispatch of AC-DC Hybrid Microgrid with Spatiotemporal Discrepancy

AC-DC hybrid microgrid with high permeability of renewable energy and its special system structure poses a challenge to the rational dispatch of AC-DC hybrid microgrid. To solve the problem, a multi-time period stochastic optimization of AC-DC hybrid microgrid scheduling strategy is proposed in this paper, which considers spatiotemporal differences. In the day-ahead optimal stage, the comprehensive consideration of the operating costs and characteristics of each distributional unit and bidirectional AC/DC power converter in the microgrid, a backup plan is developed to cope with the uncertainties of wind and AC/DC loads. In the intra-day scheduling stage, multi-scenario analysis technology is used to simulate the uncertainty of wind and AC and DC loads, and the intra-day rolling optimization dispatch model is constructed to ensure the validity of the day-ahead plan and fully suppress the power fluctuations caused by the day-ahead forecasting errors. Finally, by an example, the results show that the method can effectively deal with the influence of multi-uncertainties of the AC-DC hybrid microgrid.

Multi-timescale risk scheduling for transmission and distribution networks for highly proportional distributed energy access

With the incorporation of more renewable energy sources, distribution networks are shifting from passive to active. The interconnection of the transmission and distribution networks will increase and the tidal currents will become bidirectional, affecting the stable operation of the transmission and distribution networks. This paper proposes a multi-timescale risk dispatch study for transmission and distribution networks that have a high proportion of distributed energy access to meet the rising demand for coupling between the two types of networks. First, a day-ahead scheduling model that takes into account various operational risks is developed with the goal of minimizing total operating costs, and it optimizes the source network load and storage of multiple adjustable resources of the transmission network and distribution network jointly under the risk limit constraint. Second, an intraday scheduling model is built with the goal of minimizing the risk cost of the transmission network and distribution network, and the day-ahead scheduling plan is modified by considering the day-ahead forecast error and the different operational risks of the transmission network and distribution network. Finally, the effectiveness of the proposed multi-time-scale risk dispatch model is verified through the analysis of arithmetic cases, and it is proved that the model can enable the interaction and coordination between transmission and distribution networks as well as the synergistic optimization of operating costs and operating risks.

Short-term prediction of PV power based on fusions of power series and ramp series

Photovoltaic (PV) power generation is highly volatile and intermittent, which poses a major challenge to the prediction and intra-day scheduling of PV power. In order to effectively improve the prediction accuracy of PV power, this paper proposes a short-term prediction model through the fusion of power series and ramp series based on CNN-LSTM models. In the proposed hybrid model, both the power series prediction module and the ramp series prediction module are used to simultaneously predict the PV power, and then the results of both modules are combined by fully connected layers and corrected by a residual network to obtain the final prediction results. The prediction effects of the proposed model are illustrated by a multi-level evaluation system of prediction errors, including the evaluation of overall prediction errors, local prediction errors, and ramp point prediction errors. Case studies show that the ramp series of PV power can better characterize the fluctuation of PV power and that the proposed model has a better prediction effect than other models.

Demand response scheduling of copper production under short-term electricity price uncertainty

Marketing demand response of industrial processes on electricity spot markets can reduce operational cost. We apply our simultaneous day-ahead and intraday electricity market participation approach (Germscheid et al., AIChE J. 2022;68:e17828) to analyze the demand response potential of power-intensive copper production with our previously derived resource–task network model (Röben et al., J. CLEAN. PROD. 2022;362:132221). Specifically, we tackle intraday price uncertainty by stochastic scheduling optimization considering both risk-neutral and risk-averse market participation. Risk-averse participation allows for 1.9% weekly savings compared to only participating on the day-ahead market. Risk-neutral participation allows for 6.4% savings, but is connected to higher financial risks on weekends. Moreover, we show that load-shifting capabilities significantly depend on modeling on/off decisions either as first or second-stage integer decisions and that sequential day-ahead and intraday scheduling allows to participate in both markets while balancing financial risk, expected cost, and computational complexity compared to stochastic scheduling.

Optimal Scheduling of Power Systems with High Proportions of Renewable Energy Accounting for Operational Flexibility

The volatility and uncertainty of high-penetration renewable energy pose significant challenges to the stability of the power system. Current research often fails to consider the insufficient system flexibility during real-time scheduling. To address this issue, this paper proposes a flexibility scheduling method for high-penetration renewable energy power systems that considers flexibility index constraints. Firstly, a quantification method for flexibility resources and demands is introduced. Then, considering the constraint of the flexibility margin index, optimization scheduling strategies for different time scales, including day-ahead scheduling and intra-day scheduling, are developed with the objective of minimizing total operational costs. The intra-day optimization is divided into 15 min and 1 min time scales, to meet the flexibility requirements of different time scales in the power system. Finally, through simulation studies, the proposed strategy is validated to enhance the system’s flexibility and economic performance. The daily operating costs are reduced by 3.1%, and the wind curtailment rate is reduced by 4.7%. The proposed strategy not only considers the economic efficiency of day-ahead scheduling but also ensures a sufficient margin to cope with the uncertainty of intra-day renewable energy fluctuations.