Dispatch Method Research Articles

Data-Driven Day-Ahead Dispatch Method for Grid-Tied Distributed Batteries Considering Conflict Between Service Interests

The rapid advancement of battery technology has drawn attention to the effective dispatch of distributed battery storage systems. Batteries offer significant benefits in flexible energy supply and grid support, but maximising their cost-effectiveness remains a challenge. A key issue is balancing conflicts between intentional network services, such as energy arbitrage to reduce the overall electricity costs, and unintentional services, like fault-induced unintentional islanding. This paper presents a novel dispatch methodology that addresses these conflicts by considering both energy arbitrage and unintentional islanding services. First, demand profiles are clustered to reduce uncertainty, and uncertainty sets for photovoltaic (PV) generation and demand are derived. The dispatch strategy is originally formulated as a robust optimal power flow problem, accounting for both economic benefits and risks from unresponsive islanding requests, alongside energy loss reduction to prevent a battery-induced artificial peak. Last, this paper updates the objective function for adapting possible long-run competition changes. The IEEE 33-bus system is utilised to validate the methodology. Case studies show that, by considering the reserve for possible islanding requests, a battery with limited capacity will start to discharge after a demand drop from the peak, leading to the profit dropping from USD 185/day (without reserving capacity) to USD 21/day. It also finds that low-resolution dynamic pricing would be more appropriate for accommodating battery systems. This finding offers valuable guidance for pricing strategies.

Optimal power dispatch method for wind farms considering service quality and available power

AbstractThis paper proposes an active and reactive power dispatch method for a wind farms (WF) considering the real‐time service quality and the available power to achieve the fair power allocation within the WF. The goal is to ensure operational safety while meeting the power demand instructions from the dispatch centre. This paper generates WF service quality metrics based on real‐time monitoring data of key components to characterize the operational stability of the wind turbines (WTs). Specifically, service quality metrics related to active/reactive power are constructed separately to improve the accuracy of service quality assessment and power dispatch. The available power is then integrated into the service quality metrics to derive active/reactive power allocation factors, considering operational safety and power margin. Active power allocation is constrained by the power demand instructions, while reactive power allocation aims to maintain the WT terminal voltage within a feasible range. The proposed method can ensure the fair power allocation within WFs to meet the requirements of equipment reliability, voltage safety, and power supply simultaneously, which can improve resource utilization and avoid the potential failure. Case studies in MATLAB/Simulink confirm the effectiveness of the proposed method.

Enhancing efficiency and economy of hydrogen-based integrated energy system: A green dispatch approach based on exergy analysis

Hydrogen-based integrated energy system holds promise for leveraging the low carbon advantage of hydrogen, but inefficiency of electrolysis hydrogen production leads to significant exergy loss within its operation. Addressing this, this paper focuses on the energy-saving and economic operation of the hydrogen-based integrated energy system, proposes a novel green dispatch approach using loss cost as the objective function and establishes a refined loss cost accounting model based on exergy analysis theory. Firstly, derived from the analogy of exergy loss, the concept and accounting framework of system loss cost are proposed. Secondly, employing exergy analysis and consistent unit exergy cost principle, equipment operation loss cost is calculated through energy, exergy flows analysis and cost allocation. Thirdly, utilizing the total of equipment operation loss cost, wind curtailment penalty cost, and transmission line loss cost as the objective function, a green dispatch model is formulated to minimize overall loss cost while ensuring energy balance and other constraints. Finally, simulations analysis are conducted. The results show that compared to economic dispatch, minimized exergy loss dispatch and multi-objective dispatch methods, proposed approach increases exergy efficiency by 0.54 %, reduces cost by 6.13 % and increases the scale of hydrogen utilization by 3.45 %, respectively.

Day-ahead optimal dispatch method of integrated electric-heat-cool-gas energy system based on N-1 safety criterion

With the continuous development of integrated energy system and the deepening of the coupling relationship between multiple energy flows, the safe and stable operation of the system has received widespread attention. At present, the security of integrated energy system is mostly researched from the perspective of power grid and rarely researched from the perspective of equipment. This study proposes a day-ahead optimal dispatch method of integrated electric-heat-cool-gas energy system based on N-1 safety criterion, which ensures the normal operation of integrated energy system after the failure of a power supply equipment by increasing appropriate amount of power supply. In this study, the changes in equipment power and operating cost before and after equipment failure under the dispatch method considering N-1 safety criterion and the changes in equipment power and operating cost before and after equipment failure under the dispatch method without considering N-1 safety criterion are investigated by numerical simulation. The results show that when a power supply equipment fails, the average daily loss of load is 6139.99 kW for the dispatch method without considering the N-1 safety criterion and the average daily loss of load is 0 kW for the dispatch method considering the N-1 safety criterion. Therefore, the dispatch method considering the N-1 safety criterion performs better in terms of reliability. When a power supply equipment fails, the average operating cost is 5127.71 USD for the dispatch method without considering the N-1 safety criterion and the average operating cost is 4125.56 USD for the dispatch method considering the N-1 safety criterion. Therefore, the dispatch method considering the N-1 safety criterion performs better in terms of economy.

Asymmetric Optical Scanning Holography Encryption with Elgamal Algorithm

This paper proposes an asymmetric scanning holography cryptosystem based on the Elgamal algorithm. The method encodes images with sine and cosine holograms. Subsequently, each hologram is divided into a signed bit matrix and an unsigned hologram matrix, both encrypted using the sender’s private key and the receiver’s public key. The resulting ciphertext matrices are then transmitted to the receiver. Upon receipt, the receiver decrypts the ciphertext matrices using their private key and the sender’s public key. We employ an asymmetric single-image encryption method for key management and dispatch for securing imaging and transmission. Furthermore, we conducted a sensitivity analysis of the encryption system. The image encryption metrics, including histograms of holograms, adjacent pixel correlation, image correlation, the peak signal-to-noise ratio, and the structural similarity index, were also examined. The results demonstrate the security and stability of the proposed method.

Data-driven two-stage scheduling of multi-energy systems for operational flexibility enhancement

The multi-energy system, encompassing electricity networks, district heating networks (DHNs), and hydrogen-enriched compressed natural gas (HCNG) networks, provides an alternative for enhancing operational flexibility. This paper investigates a data-driven two-stage scheduling strategy of multi-energy system to promote uncertain renewable energy integration and improve economic benefits. Dynamic models for DHNs and HCNG networks are established, and the flexibility of multi-energy system is quantified through distribution-level power aggregation. To compromise flexibility enhancement and operational cost reduction, the multiobjective day-ahead scheduling optimization is conducted based on adaptive batch-ParEGO method. Taking day-ahead scheduling as a baseline, a data-driven real-time dispatch method is proposed based on the deep deterministic policy gradient algorithm, which adaptively modifies the energy management scheme in smaller temporal dimension to address uncertainties on both the load and source sides. The performance of the proposed two-stage scheduling method is demonstrated through numerical tests.

Distributed Cooperative Dispatch Method of Distribution Network with District Heat Network and Battery Energy Storage System Considering Flexible Regulation Capability

Flexible resources, including district heat networks (DHN) and battery energy storage systems (BESS), can provide flexible regulation capability for distribution networks (DN), thereby increasing the absorption capacity for renewable energy. In order to improve the operation economy of DN and ensure the information privacy of different operators, a distributed cooperative dispatch method of DN with DHN and BESS considering flexible regulation capability is proposed. First, a distributed cooperative dispatch framework of DN-DHN-BESS is constructed. Then, an optimal dispatch model of DHN under constant flow-variable temperature control strategy is established in order to utilize the heat storage capacity to provide flexible regulation capability for DN. Next, the optimal dispatch models of BESS and DN are established with the objective of minimizing the operation cost, respectively. Finally, a solution method based on the alternating direction multiplier method of distributed cooperative dispatch for DN-DHN-BESS is proposed. Case studies are performed on a system consisting of a 33-node DN and a 44-node DHN, and simulation results demonstrate that the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, and the proposed method reduces the total system cost by 34.5% compared to that of the independent dispatch method.

A reliable deterministic method for multi-area economic dispatch considering power losses

This paper offers a reliable deterministic method for solving multi-area economic dispatch (MAED). The proposed method can handle nonlinear power losses in electrical networks and discrete and nonlinear characteristics in power plants, such as fuel change facilities and nonconvex cost functions. To this end, the presented method reformulates the products of two variables in the power loss terms as one-dimensional expressions. Then, it converts all nonlinear terms in the MAED into linear segments. Moreover, it handles discrete decisions in fuel selection through binary variables. We assess the performance of the proposed method by some MAED test systems. The results show that the method can reliably obtain accurate solutions in the adopted test systems. It converges to costs of 10604.7, 654.7, 121592.7, 124628.4, and 169528.9 respectively in 2-area 4-unit, 3-area 10-unit, 4-area 40-unit, 2-area 40-unit, and 10-area 130-unit. These findings reproduce or outperform results found in earlier optimization algorithms. Moreover, the computational time evaluations indicate that the method can solve the adopted MAED problems in less than one minute, showing its efficiency. Overall, the obtained results confirm the efficacy of the method’s efficacy in handling the MAED with discrete and nonlinear terms.

Low carbon dispatch method for hydrogen-containing integrated energy system considering seasonal carbon trading and energy sharing mechanism

The increase in the share of renewable energy sources in the integrated energy system (IES) has led to the bidirectional flow of electricity and carbon in the transmission and distribution network, which has increased dispatch complexity. To address these issues, this paper proposes an optimal scheduling method for IES considering the seasonal carbon trading mechanism and electricity-carbon quota sharing method. First, an energy hub model of the integrated electric-heat-carbon energy system was established, with the introduction of carbon flow. Then, a seasonal carbon trading mechanism that considers the characteristics of carbon emissions in each period of the system is proposed, and the ladder-type carbon price model and carbon quota allocation method are optimized. Moreover, to fully utilize the interaction between electricity and carbon to achieve low-carbon economic dispatch, a dual time-scale electricity-carbon quota sharing method is proposed. Finally, the effectiveness of the proposed method is verified by the case studies. The results show that the scheduling method considering seasonal carbon trading mechanism and electricity-carbon quota energy sharing can optimize the allocation of resources such as electricity and carbon quota among the systems, and reduce the cost of the system by 6.04 % and carbon emission by 4.27 %.

Analysis of power load tracking and regulation performance in a distributed multi-energy coupled system with nuclear and solar sources

This paper presents a novel distributed multi-energy coupled system that combines solar PV, nuclear power, and energy storage systems to address the power supply challenges in remote regions. Through parameter analysis and multi-objective optimization, the study aims to minimize the need for frequent reactor adjustments during system operation. The key findings indicate that by regulating the rotational speed of the main-compressor, it is possible to meet the power load requirements for a single nuclear power system. The battery capacity and the number of PV panels have a significant impact on the adjustment frequency of the reactor and the power output of the nuclear power system, respectively. The optimal battery capacity is determined to be 183 kWh, while the optimal number of PV panels is 327. These configurations result in an average power output of 20.86 kW for the PV system and 3458.28 kW for nuclear power system. To maximize the utilization of PV energy and minimize reactor adjustments, an energy dispatch strategy is proposed. With the optimal configuration, the adjustment frequency of the reactor decreases to 339 and 276 during the winter and summer months, respectively. Overall, this paper offers a feasible configuration and energy dispatch method for regional power supply.

Collaborative robust dispatch of electricity and carbon under carbon allowance trading market

The launch of the carbon-allowance trading market has changed the cost structure of the power industry. There is an asynchronous coupling mechanism between the carbon-allowance-trading market and the day-ahead power-system dispatch. In this study, a data-driven model of the uncertainty in the annual carbon price was created. Subsequently, a collaborative, robust dispatch model was constructed considering the annual uncertainty of the carbon price and the daily uncertainty of renewable-energy generation. The model is solved using the column-and-constraint generation algorithm. An operation and cost model of a carbon-capture power plant (CCPP) that couples the carbon market and the economic operation of the power system is also established. The critical, profitable conditions for the economic operation of the CCPP were derived. Case studies demonstrated that the proposed low-carbon, robust dispatch model reduced carbon emissions by 2.67% compared with the traditional, economic, dispatch method. The total fuel cost of generation decreases with decreasing, conservative, carbon-price-uncertainty levels, while total carbon emissions continue to increase. When the carbon-quota coefficient decreases, the system dispatch tends to increase low-carbon unit output. This study can provide important guidance for carbon-market design and the low-carbon-dispatch selection strategies.

A Privacy-Preserving Distributed Economic Dispatch Method for Integrated Port Microgrid and Computing Power Network

A Privacy-Preserving Distributed Economic Dispatch Method for Integrated Port Microgrid and Computing Power Network

A Low-Carbon Economic Dispatch Method for Power Systems With Carbon Capture Plants Based on Safe Reinforcement Learning

A Low-Carbon Economic Dispatch Method for Power Systems With Carbon Capture Plants Based on Safe Reinforcement Learning

Robust optimization dispatch for PV rich power systems considering demand response and energy storage systems

In recent years, the ever-rising penetration of distributed photovoltaics (PV) power has presented substantial challenges in power system dispatch due to its inherent randomness and unpredictability. To bridge this gap, this paper proposes a two-stage robust optimization method for power system security dispatch considering traditional generators as well as flexible resources, such as load demand response and energy storage systems. Specifically, a price-based demand response model is established to optimize the system’s load curve during a day. On this basis, a two-stage optimization problem for day-ahead and intra-day power system dispatch model is proposed. The dispatch objective is to minimize the overall cost in worst-case scenarios through properly scheduling unit commitment (UC) as well as flexible resources in each dispatch interval. Column and constraint generation (C&CG) algorithm is adopted for problem solving. The effectiveness of the proposed method is validated by case studies based on a modified 6-node system and a 24-node system. Simulation results indicate that through appropriately scheduling the energy storage system and load demand response, the proposed dispatch method can significantly reduce the total operation cost of a PV rich power system, which in turn facilitates the integration of PV power.

Dynamic Consensus-Based ADMM Strategy for Economic Dispatch with Demand Response in Power Grids

This paper introduces a dynamic consensus-based economic dispatch (ED) algorithm utilizing the Alternating Direction Method of Multipliers (ADMM) to optimize real-time pricing and generation/demand decisions within a decentralized energy management framework. The increasing complexity of modern energy markets, driven by the proliferation of Distributed Energy Resources (DER) and variable demands from hybrid electric vehicles, necessitates a departure from traditional centralized dispatch methods. This research proposes a novel ADMM-based solution tailored for non-responsive and responsive demand units that integrates demand response mechanisms to adaptively manage real-time fluctuations while enhancing security and privacy through distributed data management. The testing of the algorithm on the IEEE 39 bus system under various load conditions over 24 h demonstrated the algorithm’s effectiveness in handling traditional and renewable energy sources, particularly highlighting the economic benefits of shifting controllable loads to periods of low-cost renewable availability. The findings underscore the algorithm’s potential to reduce energy costs, enhance energy efficiency, and offer a scalable solution across diverse grid systems, contributing significantly to advancing global energy policy and sustainable management practices.

Distributed Dispatching Optimization based on Consistency Theory for Smart Grid

Abstract The energy management issue of a smart grid is the key to the efficient and stable operation of a smart grid. Due to the large number of distributed energy sources connected, the centralized dispatch method is no longer applicable, and the smart grid uses distributed algorithms for dispatch. In this paper, based on modeling and analysis of the smart grid dispatch problem, the distributed dispatch algorithm is designed by combining the consistency theory in multi-intelligent body systems. First, the distributed scheduling algorithm is designed under ideal communication conditions. Then, the correctness of the algorithm is verified in the MATLAB simulation platform under two conditions without considering the generator set output constraint and considering the generator set output constraint. The analysis of the study simulation results shows that the optimal solution of the scheduling problem is obtained when the marginal costs of the generation equipment converge consistently.

Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.

Intelligent extraction of reservoir dispatching information integrating large language model and structured prompts

Reservoir dispatching regulations are a crucial basis for reservoir operation, and using information extraction technology to extract entities and relationships from heterogeneous texts to form triples can provide structured knowledge support for professionals in making dispatch decisions and intelligent recommendations. Current information extraction technologies require manual data labeling, consuming a significant amount of time. As the number of dispatch rules increases, this method cannot meet the need for timely generation of dispatch plans during emergency flood control periods. Furthermore, utilizing natural language prompts to guide large language models in completing reservoir dispatch extraction tasks also presents challenges of cognitive load and instability in model output. Therefore, this paper proposes an entity and relationship extraction method for reservoir dispatch based on structured prompt language. Initially, a variety of labels are refined according to the extraction tasks, then organized and defined using the Backus–Naur Form (BNF) to create a structured format, thus better guiding large language models in the extraction work. Moreover, an AI agent based on this method has been developed to facilitate operation by dispatch professionals, allowing for the quick acquisition of structured data. Experimental verification has shown that, in the task of extracting entities and relationships for reservoir dispatch, this AI agent not only effectively reduces cognitive burden and the impact of instability in model output but also demonstrates high extraction performance (with F1 scores for extracting entities and relationships both above 80%), offering a new solution approach for knowledge extraction tasks in other water resource fields.

Enhancing Power Supply Flexibility in Renewable Energy Systems with Optimized Energy Dispatch in Coupled CHP, Heat Pump, and Thermal Storage

The use of renewable energy by converting it into heat is an important form of storing energy in a usable form and improving the energy supply flexibility; therefore, the electricity–heating system (EHS) can cope with load fluctuations. However, relevant research is lacking on improving the energy supply limitations by the optimal dispatch of energy flow at the typical EHS, such as the coupled CHP–heat pump–thermal storage system (CCHTS). Based on the study of the energy supply characteristics of the CCHTS for extending the energy supply limitation, this study develops an optimal dispatch method using a heat pump (HP) and the thermal storage (TS) of heating networks to improve the flexibility of the CCHTS and the accommodation capacity of renewable energy. The maximum and minimum energy supply limitation model of the CCHTS and the output power characteristic model are established. Based on the piecewise power supply constraint, the energy flow of the EHS is optimized by using the quadratic programming algorithm. The CCHTS can significantly improve the energy supply flexibility; both coupled combined heat and power (CHP) + HP and coupled CHP + TS can improve the power supply flexibility, but the enhanced effect of CHP + HP is better than that of CHP + TS. An increase of 7.6% in wind power consumption is achieved. The consumption of renewable energy increases by 17.9% in the energy flow optimization results.

An enhanced NSGA-II and a fast constraint repairing method for combined heat and power dynamic economic emission dispatch

In this paper, an enhanced NSGA-II (ENSGA-II) is presented for the combined heat and power dynamic economic emission dispatch (CHPDEED). ENSGA-II produces offspring individuals by a novel crossover of the Cauchy distribution with adaptive location and scale parameters. It can not only sufficiently explore and exploit the decision space but also remain a relatively high population diversity. Also, ENSGA-II adopts a modified crowding distance to penalize crowded individuals, seeking to evenly distribute the individuals in the objective space. In addition, a fast constraint repairing method (FCRM) is proposed to significantly reduce the constraint violations and guide infeasible individuals to move towards feasible zones rapidly. The proposed ENSGA-II is applied to a number of multi-objective optimization problems and compared with the other eight multi-objective evolutionary algorithms (MOEAs). ENSGA-II is found to produce better results on most problems, such as smaller inverse generational distances, larger hypervolumes, larger coverage rates and smaller spacings. Therefore, ENSGA-II can obtain a satisfactory Pareto set with broad spread, high diversity, strong convergence and good evenness, and it is an efficient alternative for CHPDEED.