Optimal Dispatch Strategy Research Articles

Optimal dispatch scheme considering system operational flexibility

To enhance the system's capacity to manage emergencies and foster the efficient utilization of newly generated energy, this paper introduces a redefined concept of system operation flexibility and proposes a two-stage optimized economic dispatch model. This model is tailored to ensure the system's operational flexibility. In this method, net load is pinpointed as the primary driver of flexibility demand, and a data-driven approach is employed to capture the uncertainties associated with net load across various risk levels. As flexibility supplier, conventional units work in tandem to optimize their operating points, fulfilling flexibility demand while adhering to the Do Not Exceed (DNE) baseline, thereby ensuring that system flexibility remains within safe limits. A case study on the modified SG-126 bus system demonstrates that the proposed methodology effectively highlights flexibility shortfalls and validates its efficiency in terms of cost and inter-regional coordination.

Hierarchical Optimal Dispatching of Electric Vehicles Based on Photovoltaic-Storage Charging Stations

Electric vehicles, known for their eco-friendliness and rechargeable–dischargeable capabilities, can serve as energy storage batteries to support the operation of the microgrid in certain scenarios. Therefore, photovoltaic-storage electric vehicle charging stations have emerged as an important solution to address the challenges posed by energy interconnection networks. However, electric vehicle charging loads exhibit notable randomness, potentially altering load characteristics during certain periods and posing challenges to the stable operation of microgrids. To address this challenge, this paper proposes a hierarchical optimal dispatching strategy based on photovoltaic-storage charging stations. The strategy utilizes a dynamic electricity pricing model and the adaptive particle swarm optimization algorithm to effectively manage electric vehicle charging loads. By decomposing the dispatching task into multiple layers, the strategy effectively solves the problems of the “curse of dimensionality” and slow convergence associated with large numbers of electric vehicles. Simulation results demonstrate that the strategy can effectively achieve peak shaving and valley filling, reducing the load variance of the microgrid by 24.93%, and significantly reduce electric vehicle charging costs and distribution network losses, with a reduction of 92.29% in electric vehicle charging costs and 32.28% in microgrid losses compared to unorganized charging. Additionally, this strategy can meet the travel demands of electric vehicle owners while providing convenient charging services.

Three-stage resilience enhancement via optimal dispatch and reconfiguration for a microgrid

Extreme weather causes an increase in power system failure. Previous studies on system resilience have often overlooked the user-side of a microgrid. This study proposes composite resilience indices (RI) based on the power supply of a large-scale manufacturing campus microgrid to quantify its ability to withstand extreme events. The proposed RI consider load priority, minimum supply load, total energy supplied, and the performance recovery-to-degradation slope ratio in an islanding microgrid. Accordingly, this study presents a three-stage resilience optimal dispatch and reconfiguration strategy, including energy-level scheduling, grid-level reconfiguration, and dynamic-level verification. A multi-objective optimization approach is used for energy scheduling, followed by system reconfiguration via DIgSILENT system modeling to meet the grid code and maximize load supply. Value at Risk methods are used to verify microgrid stability and load shedding requirements, supported by the virtual synchronous generator control of the energy storage system. The test results from a practical large-scale manufacturing campus microgrid validate the proposed approaches for enhancing system resilience considering load values.

Toward on Rolling Optimal Dispatch Strategy Considering Alert Mechanism for Antarctic Electricity-Hydrogen-Heat Integrated Energy System

Toward on Rolling Optimal Dispatch Strategy Considering Alert Mechanism for Antarctic Electricity-Hydrogen-Heat Integrated Energy System

Strategic investments in mobile and stationary energy storage for low-carbon power systems

The main feature and trend of the distribution system is the integration of renewable energy with high penetration rates. The variability and zero marginal cost characteristics of renewable energy will lead to drastically fluctuating locational marginal prices. In the deregulated electricity market, merchants have incentives to utilize energy storage and price arbitrage. Mobile energy storage has a short capital payback period and is widely recognized for transferring energy in the temporal and spatial dimensions. This paper analyses the interaction between merchants and distribution system operators and presents a hybrid energy storage strategic investment framework using bi-level programming. In the upper-level problem, the merchant formulates the capacity, location, and operation strategy of different energy storage to maximize the market revenue of hybrid energy storage, which is paid for by the locational marginal price. In the lower-level problem, the distribution system operator develops an optimal dispatch strategy considering renewable energy and merchant investments. A joint energy and reserve market clearing procedure is performed to derive the locational marginal prices. In the lower-level problem, a data-driven solution strategy based on machine learning is introduced. It uses deep neural networks to approximate the mapping relationship between distribution system states and locational marginal prices. The presented methodology is implemented in an IEEE-33 node system. The results demonstrate that, compared to the basic case, the hybrid energy storage investment strategy has led to an 8.1 % increase in merchant profits and a 12.9 % reduction in the operational costs of the distribution network. The deep neural network approximator fully avoids the lower-level problem with less than 5 % optimality loss.

Performance analysis of a medical waste gasification-based power generation system integrated with a coal-fired power unit considering dispatching optimization

This paper collects into a medical waste plasma gasification, a cleaning system, a gas turbine, a syngas storage tank and a coal-fired power unit to establish the medical waste plasma hybrid peak shaving system. In this system, medical waste is transported into a plasma gasification furnace and prepared as syngas by reaction. Then, the syngas is delivered to the gas turbine after cooling and deacidification. Finally, the flue gas from the combustion of the gas turbine is used to heat the condensate of a coal-fired power unit. Compared to the coal-fired power unit, the system has a larger range of peak load capability. Establish the integrated regional power grid dispatching model. The paper selects five typical days, which combine the actual data to comprehensively consider many factors. Then analyze the optimal economy of the regional power grid and propose the optimal dispatch scheme for the regional power grid. After modification, the system energy efficiency is 37.38 %, and system exergy efficiency is 36.19 %. After the dispatching optimization, the average daily profit is 140.78 k$, the profitability can reach initial cost in 6.20 years, and the net present value generated by the medical waste plasma hybrid peak shaving system is 178,410.43 k$.

Optimizing peak-shaving cooperation among electric vehicle charging stations: A two-tier optimal dispatch strategy considering load demand response potential

The increase in the grid connection of electric vehicles (EVs) provides great potential for peak load regulation and valley filling of the grid. In order to solve the challenges brought by the integration of new energy vehicles into the power grid and give full play to the potential of EV demand response, this paper proposes a two-layer optimal dispatch strategy for the “distribution network-charging station” system. First, assess EV load demand response potential. The research area is divided into different functional areas by using the data of urban interest points, and the travel habits of users are analyzed by using EV driving data. On this basis, considering the influencing factors such as EV battery capacity (SOC), charging electricity price and spatial characteristics, the EV load response potential evaluation model is constructed by integrating user electricity price response and charging power response. Secondly, taking the evaluation value of EV response potential as the range of load adjustment, in order to optimizing peak-shaving cooperation among EV charging stations and obtaining the peak-shaving measures of each charging station, a “distribution network-charging station” double-layer optimal dispatch is carried out. The upper-level distribution network scheduling aims at minimizing the unfinished peak shaving rate and distribution network loss, and optimizes the scheduling power allocation of the peak tasks of each charging station. The lower-level charging station scheduling is based on the principle of maximizing user charging satisfaction and charging station economic benefits, and completes the peak-shaving scheduling determined by the superior. Finally, the strategy is applied in a specific area of Shaanxi Province, and the scheduling results show that the strategy is more economically feasible.

Optimal dispatch of unbalanced distribution networks with phase-changing soft open points based on safe reinforcement learning

Distributed energy resources and uneven load allocation cause the three-phase unbalance in distribution networks, which may harm the health of power equipment and increase the operational costs. There is emerging opportunity to dispatch soft open points to improve the operation performance of active distribution network. This paper proposes an optimal dispatch strategy to improve the network balancing performance, where a new type of phase-changing soft open point is installed. First, a new type of phase-changing soft open point with full-phase changing ability is introduced to balance the three-phase power flow. Then, the optimization model is formulated for phase-changing soft open points dispatching to minimize the total cost of distribution network. Furthermore, the model is formed as a constrained Markov decision process and efficiently solved by the augmented Lagrangian-based safe deep reinforcement learning algorithm featuring the soft actor-critic method. Finally, numerical simulations are conducted to validate the effectiveness, accuracy, and efficiency of the proposed method.

Research on Multi-Microgrid Electricity–Carbon Collaborative Sharing and Benefit Allocation Based on Emergy Value and Carbon Trading

In response to climate change, the proportion of renewable energy penetration is increasing daily. However, there is a lack of flexible energy transfer mechanisms. The optimization effect of low-carbon economic dispatch in a single park is limited. In the context of the sharing economy, this study proposes a research method for multi-park electricity sharing and benefit allocation based on carbon credit trading. Firstly, a framework for multi-park system operation is constructed, and an energy hub model is established for the electrical, cooling, and heating interconnections with various energy conversions. Secondly, a park carbon emission reduction trading model is established based on the carbon credit mechanism, further forming an optimal economic and environmental dispatch strategy for multi-park electricity sharing. Matlab+Gurobi is used for solving. Then, based on asymmetric Nash bargaining, the comprehensive contribution rate of each park is calculated by considering their energy contribution and carbon emission reduction contribution, thereby achieving a fair distribution of cooperation benefits among multiple parks. The results show that the proposed method can effectively reduce the overall operational cost of multiple parks and decrease carbon emissions, and the benefit allocation strategy used is fair and reasonable, effectively motivating the construction of new energy in parks and encouraging active participation in cooperative operations by all parks.

Distributed predefined-time optimal economic dispatch for microgrids

With the massive popularization of distributed generators, optimal economic dispatch has been a key optimization problem to maintain stable and efficient work of the whole system. In this paper, a new smooth reconstruction penalty function with continuous and piecewise linear differential is designed to deal with generation power constraints, which promotes to obtain a better suboptimal solution compared with the existing smooth penalty methods. A distributed predefined-time optimal economic dispatch strategy is presented by utilizing a time-based function. By employing the proposed strategy, the minimization of the generation cost with transmission loss under the power balance constraint and generation minimum/maximum constraints can be realized within a predefined settling time. The performance of the proposed optimization strategy is evaluated by simulations and hardware-in-the-loop experiments in terms of validity verification, robustness to load change and topology reconfiguration, plug-and-play functionality, and comparison with the existing results to illustrate the advantages of fast convergence and near optimal results.

Hybrid-timescale optimal dispatch strategy for electricity and heat integrated energy system considering integrated demand response

An integrated energy system realizes multi-energy management and multi-load integrated supplement. However, because of the uncertainties of the renewable energy and loads and different dynamic characteristics of multi-energy, the efficiency of the system is constrained. To ascend the flexibility, a hybrid-timescale dispatch strategy considering integrated demand response is proposed. Firstly, a deep integrated demand response strategy is proposed using price demand response and ground source heat pump. Secondly, a linear dynamic model of the heat network is adopted to calculate the real-time energy transfer status in the heat pipeline, reflecting the thermal delay. Thirdly, to embed the linear dynamic model into the optimal scheduling process, a hybrid-timescale optimal dispatch framework for electricity and heat integrated energy system is proposed. Simulation results on MATLAB show that compare to the system that does not consider integrated demand response, the wind desertion, user cost and operation cost are reduced by 16.4 %, 0.85 %, and 2.72 %, respectively. And compared with the system only using energy storage, the planning cost is reduced by 47.0 %. By introducing the IDR strategy, and adopting the dynamic model of heat system, the flexibility of the system is released and the balance between economic cost and energy efficiency are achieved.

An optimal dispatch strategy of off-grid park integrated energy system considering source volatility

An off-grid integrated energy system (IES) with hydrogen storage at park-level is proposed, utilizing wind, solar and natural gas as the main energy supply to replace fossil fuels, in order to overcome the insufficient consideration of energy source conversion and information exchange in the traditional energy system. Firstly, an IES schematic consisted with typical devices is proposed, and all the components are modelled in detail. Then, based on the renewable energy (RE) input requirements and the facility parameters, an optimal dispatch model for multi-energy conversion based on hydrogen storage has been demonstrated. In addition, an improved particle swarm optimization algorithm is also introduced to improve the strategy. Combined with the environmental characteristics, the multi-objective solution with the lowest economic cost and environmental cost, the highest energy supply reliability and energy efficiency as the optimization objectives is determined. Finally, a case study is conducted using the practical data of the Chongli Large-Scale Wind-Solar Complementary Coupled Hydrogen Production System Application Demonstration Project to validate the feasibility of the study under real conditions. The result shows that the RE consumption rate is greater than 99 % under the premise of considering the environmental cost and ensuring the operation reliability, which can effectively improve the operation economy and user economy of the power grid

Synergizing renewable energy sources in building-integrated hybrid energy systems via niche-ant colony optimization

The rapid advancement of Building-Integrated Hybrid Energy Systems (BIHES), characterized by renewable energy sources with variable generation patterns, presents significant challenges for effective integration and utilization. This paper introduces an optimal economic dispatch strategy for commercial BIHES using a Niche-Ant Colony Optimization (NACO) Algorithm. BIHES integrates renewable sources such as 200 kW photovoltaic panels, 150 kW wind turbines, and 500 kWh energy storage systems within commercial buildings. A detailed mathematical model for economic dispatch leverages a multi-load demand of 1.2 MW and flexible adjustment capabilities. The model aims to maximize on-site renewable energy use while balancing grid interaction. The NACO Algorithm, tested through comprehensive simulations, demonstrated significant improvements, resulting in a 12.96 % reduction in environmental costs, an 18.4 % reduction in the Levelized Cost of Energy (LCOE), and a 14.25 % reduction in total operational costs compared to traditional methods. Additionally, the system achieved annual CO2 savings of 25,000 tons, increased renewable energy absorption by 22 %, reduced grid dependency by 30 %, and decreased energy purchase costs by 20 %. Energy storage optimization also extended the storage system's lifecycle by 15 %. These results highlight the enhanced synergy and complementarity of multiple energy sources within BIHES, showcasing its potential for substantial economic and environmental benefits.

Low-carbon optimal dispatch strategy of power systems considering the combined operation of oxy-fuel combustion and electric hydrogen production

ABSTRACT Oxy-fuel combustion technology offers a promising solution for achieving nearly zero carbon dioxide emissions from coal-fired units, presenting a viable path toward low-carbon dispatch. This study establishes a simulation model for the low-carbon operation of power systems, focusing on the integrated operation of an oxy-fuel combustion plant and electric hydrogen production. By incorporating oxygen generated from electric hydrogen production into the oxy-fuel combustion plant, the primary objective is to minimize system operation costs. Through simulation analysis and verification, a comparative scenario is constructed to evaluate performance. The simulation results indicate that the joint operation approach yields significant benefits. In comparison to separate operation, the joint operation strategy leads to a reduction of approximately 7.38% in total system costs, 18.79% in operating costs, and 23.56% in carbon emissions. This integrated approach not only achieves lower carbon emissions but also results in reduced overall system costs.

Dispatch strategies for large-scale heat pump based district heating under high renewable share and risk-aversion: A multistage stochastic optimization approach

Heat pumps play an essential role in decarbonizing the heating sector, and their adoption is projected to rise significantly. The high share of large-scale heat pumps in district heating exposes heating utilities to uncertainty in electricity markets. This challenge is further exacerbated by 1) future high share of renewable energy resulting in increased uncertainty of electricity prices, and 2) introduction of voluntary energy bids for balancing energy markets (or regulation market). Despite the importance of this issue, little research has been conducted to address it. Therefore, this paper investigates the optimal dispatch of large-scale heat pump based district heating by adopting multi-stage stochastic optimization approach to minimize the total expected heat generation cost, taking sequential electricity markets prices as stochastic. We also evaluate the impact of high renewable energy penetration on this optimal dispatch strategy. Furthermore, we include the risk preference of district heating operator by using conditional value at risk in the objective function. We conclude that heat pump should be coupled with flexible technologies like electric boilers as more renewable energy and balancing market trading increases flexible units' dispatch. Also, most of the heat produced (50%) in our case study is used flexibly via thermal storage. We find more renewable could lead to negative expected costs and voluntary bids in balancing market further reduces the expected cost by 28–59%. Furthermore, trading risk increases significantly with renewable energy penetration, which can be mitigated to a limited extent by bidding in up and down balancing market. Risk aversion shifts trading from intraday to day-ahead market and promotes heat pump dispatch.

Multi-objective sizing and dispatch for building thermal and battery storage towards economic and environmental synergy

The role of building thermal and battery storage is pivotal in advancing smart cities and achieving sustainability goals through effective energy management. Despite their significance, there are several limitations in the sizing approach and value stream analysis with various objectives for their widespread adoption in buildings. This work proposes a flexible and scalable multi-objective optimization framework for optimal sizing and dispatch of building thermal and battery storage, addressing multiple objectives simultaneously using mixed-integer linear programming. The weighted-sum method is adapted, combining multiple objectives into a single function. The two-stage procedure iterates over different weights, generates optimal solutions, and forms the Pareto front. Case studies are performed to assess the energy, economic, and environmental benefits of building energy storage systems for a large office building in three climate locations. The results demonstrate that the proposed framework efficiently determines optimal sizing and dispatch strategies, addressing the balance between economic viability and emission reduction. The dynamic relationship between time-of-use energy charges and emission factors leads to diverse strategies based on whether economic or environmental concerns are prioritized. This research enhances our understanding of the benefits of thermal and battery storage systems in buildings, providing valuable guidance to stakeholders.

Optimal dispatch strategy of battery energy storage system in utility-scale photovoltaic integrated grid under variability

The frequency response of a photovoltaic (PV) system integrated power grid is severely hampered due to inadequate inertial support. Integrating a battery energy storage system (BESS) can assist in maintaining frequency response by providing a rapid injection of active power into the grid. Nevertheless, instead of typical constant droop settings enabled in BESS, adopting variable PV power dependent operation of BESS can ensure frequency stability with reduced injection, especially in PV-integrated grids. From this viewpoint, this paper proposes a novel frequency control approach of BESS depending on the available PV power in the grid. A gradient descent-based optimization is utilized to evaluate the required maximum injection from the installed BESS to maintain frequency response under different PV generation values. This approach is then tested by modifying the BESS frequency controller to keep the active power dispatch from BESS within the calculated value under a given outage. The whole methodology is analyzed in a customized IEEE 39 bus New England System under different PV penetration and generation scenarios. The results indicate successful convergence to optimal dispatch levels for the BESS across all evaluated scenarios. As PV generation increases, the optimal BESS dispatch as a percentage of rated PV output declines gradually. However, at lower PV generation, this value rises rapidly and reaches the installed BESS capacity. Later, the applicability of the methodology is validated by comparing the simulated outputs with different test grid network and another support technique- Synchronous Condenser (SC). From the standpoint of frequency stability, the proposed technique can assist the grid planners in regulating BESS operations in a renewable integrated grid more effectively.

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.

Operation characteristics analysis and optimal dispatch of solar thermal-photovoltaic hybrid microgrid for building

The optimal dispatch for hybrid microgrids is the crucial approach to decrease maintenance costs and enhance operational reliability. This paper aims to provide a feasible solution for the optimal dispatch of a solar thermal-photovoltaic hybrid microgrid. A distributed energy system of a building is established and the power load is analyzed. Operation parameters are optimized for hybrid microgrid in isolated operation mode and grid-connected operation mode. The operation performance with adequate and inadequate solar irradiation is evaluated by using fixed dispatch and optimal dispatch strategy. In addition, the performance of hybrid microgrid under power selling and purchasing conditions are analyzed. Results show that the operation mode of building hybrid microgrid can be adjusted to isolated/grid-connection modes based on actual power requirements. The optimal dispatch strategy shows great advantages on both solar adequate and inadequate conditions under isolated operation mode for hybrid microgrid, at most 14.3 % lower for maintenance than that of fixed strategy. In addition, the power selling to the external power grid can significantly improve the profit of the hybrid microgrid and generate earnings of $6102.1 per year under optimal dispatch. The research can offer an analysis approach for hybrid microgrid, and further increase clean energy utilization efficiency.

Carbon Neutrality Computational Cost Optimization for Economic Dispatch With Carbon Capture Power Plants in Smart Grid

To achieve carbon neutrality, reducing carbon emissions is crucial in dispatching problems in smart grid. Though renewable energy such as wind power has low carbon emissions, it suffers from random generation, which makes the thermal power necessary for a stable supply power system. To reduce carbon emissions, the thermal power plants are transformed into carbon capture power plants, which brings new challenges to economic dispatch algorithms. Besides, there are usually many constraints to keep the security operation of power systems, which incurs a large problem scale and high computational cost. Most existing methods either do not consider reducing carbon emissions, or suffer from high computational costs. In this paper, a framework for the carbon capture plants with wind power to reduce both running costs and carbon emissions is designed to support carbon neutrality. To reduce computational cost, initial-training and fine-tuning are used. A deep neural network is employed to describe the relationship between users' load and the constraints, which provides guides for finding the active constraints. Therefore, the problem scale can be significantly decreased, making the optimal dispatching strategy obtained quickly. The experimental results on real-world data show that the proposed framework can obtain the optimal strategy efficiently.