Proposed Dispatch Strategy Research Articles

Event-Driven Day-Ahead and Intra-Day Optimal Dispatch Strategy for Sustainable Operation of Power Systems Considering Major Weather Events

As the proportion of renewable energy installations in modern power systems increases, major weather events can easily trigger significant fluctuations in new energy generation and electricity load, presenting the system with the dual challenges of ensuring power supply and renewable energy consumption. Traditional dispatch models need more coordination and optimization of flexible resources under major weather events and risk management of system operations. This study focuses on provincial-level transmission systems, aiming to achieve the coordinated and optimized dispatch of flexible resources across multiple time scales in response to the complex and variable environments faced by the system. Firstly, by profoundly analyzing the response mechanisms of power systems during major weather events, this study innovatively proposes an event-driven day-ahead and intra-day optimal dispatch strategy for power systems. This strategy can sense and respond to major weather events in the day-ahead phase and adjust dispatch decisions in real time during the intra-day phase, thereby comprehensively enhancing the adaptability of power systems to sudden weather changes. Secondly, by considering the variability of renewable energy sources and electricity demand in the day-ahead and intra-day dispatch plans, the strategy ensures efficient and reliable power system operation under normal and major weather event scenarios. Finally, the method’s effectiveness is validated using actual data from a provincial-level power grid in China. The proposed dispatch strategy enhances the resilience and adaptability of power systems to major weather events, which are becoming increasingly frequent and severe due to climate change. The research demonstrates that an event-driven day-ahead and intra-day optimal dispatch strategy can enhance the economic efficiency and robustness of power system operations through the coordinated dispatch of flexible resources during major weather events, thereby supporting the transition toward sustainable energy systems that are resilient against the challenges of a changing climate.

Low-Carbon Economic Dispatch of Virtual Power Plant Considering Hydrogen Energy Storage and Tiered Carbon Trading in Multiple Scenarios

Reducing carbon emissions and increasing the integration of new energy sources are key steps towards achieving sustainable development. Virtual power plants (VPPs) play a significant role in enhancing grid security and promoting the transition to clean, low-carbon energy. The core equipment of the VPP, the CHP unit, utilizes a thermal engine or power station to generate electricity and useful heat simultaneously. However, the intermittent and volatile nature of renewable energy sources, as well as the “heat-driven power generation” mode of combined heat and power (CHP) units, presents contradictions that severely affect their peak-shifting capability and lead to high carbon emissions. To address these issues, a novel VPP is established by integrating traditional power plants with carbon capture and hydrogen energy storage. This approach utilizes a “hydrogen energy storage–electric boiler” decoupling method to address the operational mode of CHP, strengthens the coupling relationship between electric and thermal hydrogen loads, and considers a tiered carbon-trading mechanism. With the net profit of the VPP as the optimization objective, the model balances economic and environmental considerations and establishes a low-carbon economic dispatch model for the VPP. A genetic algorithm is employed for solving, and three different dispatch strategies are set for simulation in three distinct seasonal scenarios. The comprehensive comparative analysis of the dispatch results reveals a reduction in carbon emissions and an increase in net profit to varying degrees across all three seasons. Overall, the proposed dispatch strategy demonstrates the ability to enhance the new energy-integration capacity and total revenue of a VPP while simultaneously achieving the goal of reducing carbon emissions.

Evaluation of stand-alone hybrid renewable energy system with excess electricity minimizer predictive dispatch strategy

The prioritization of economic and reliability objectives in the optimization of stand-alone microgrids and off-grid hybrid renewable energy systems (HRES) has led to optimum systems with high excess electricity and low self-consumption indexes. Managing surplus power after charging the storage bank without imposing a negative impact on energy cost (COE) and environmental factors is a challenge for off-grid applications. In recent years the development of predictive dispatch strategies (PD), improves the energy efficiency in HRES compared to conventional dispatches such as load following (LF). However, the mismatch between renewable power production and demand profiles prohibits high excess electricity reduction. In this study, a novel dispatch strategy, entitled modified predictive dispatch (MPD), is developed by integrating a proactive approach to charging/discharging the battery bank and demand-side management (DSM) based on the upcoming short-term excess electricity profile. The results showed that with the participation of an average of less than 20 % deferrable components and the possibility of load shifting for less than 3 h, the excess electricity can be reduced to about 7.5 %, compared to 41 % and 18.5 % for LF and PD strategies, respectively. Furthermore, the self-utilization index of surplus power improved from less than 25 % to about 90 %. These improvements were achieved through the MPD strategy, which reduced COE by more than 10 %, reaching about 0.12 $/kWh without any significant effect on emission factors. The sensitivity analysis indicated that achieving an excess electricity self-utilization index of over 80 % requires a load-shifting possibility of more than 2 h and a deferrable load participation of about 10 % in the DSM approach. Therefore, results approved the effectiveness of the proposed dispatch strategy in high renewable penetration systems, even with low levels of participation by power consumers.

An IGDT-based a low-carbon dispatch strategy of urban integrated energy system considering intermittent features of renewable energy

The current global energy crisis has prompted a significant rise in the use of low carbon units, making it a crucial research trend in the field of urban energy system, especially in terms of addressing the intermittency of renewable energy generation. This study proposes an integrated energy system planning model (UIES) for urban areas. In addition, to tackle the issue of carbon emissions, a reward and punishment ladder-type carbon transaction model is presented, which aims to control the level of carbon emissions within urban areas. To evaluate the performance of the UIES model, the authors develop an objective function that integrates the sum of equipment operation and maintenance costs, equipment daily acquisition costs, purchased energy costs, carbon trading costs, and integrated demand response (IDR) costs. The stochastic features of renewable energy sources are taken into account in the proposed approach, and an information gap decision theory (IGDT) based strategy is designed to quantify the uncertainty interval and intermittent fluctuations associated with renewable energy. This approach also aims to mitigate any negative effects and variations that may occur during the urban energy system's IDR process. Finally, the simulation tests to demonstrate that the average decrease in operating cost of both models based on IGDT strategy are 7.36 %, and the peak of electrical (EL), heating (HL) and cooling load (CL) are reduced by 20.45 %, 12.38 % and 5.02 % respectively. in conclusion, the energy service providers in urban areas can effectively manage renewable energy fluctuations by independently adjusting the operation sequence of equipment, and the economy and stability of the UIES can be effectively improved by the proposed dispatch strategy.

Risk-Based Dispatch Strategy for a Power System with High Wind Power Integration Considering Damage Due to Typhoons

In the case of a coastal power system with high wind power integration, the frequent occurrence of typhoons may threaten the structural safety of the network and induce rapid ramp events of wind power. This article proposes a dispatch strategy for a power system with high wind power integration under a typhoon environment, comprehensively considering the phenomena and problems that occur in real typhoon disasters. First, the fragility curve of the tower is modeled considering the joint effects of wind and rain. Second, a filtering technique is adopted to enhance the effects of the high outage probability towers on network reliability. Third, a wind power curtailment strategy that restricts the extremely rapid ramp of wind power during typhoons is developed. Finally, both the curtailment strategy of wind power and the structural failure of the network are incorporated into the risk-based dispatch strategy during typhoon events. Analysis of the test system validates that the proposed dispatch strategy can improve the resilience of power systems during typhoons. Furthermore, the proposed method can also be used in other scenarios under extreme weather conditions, such as ice coating in power networks in which structural failures also occur frequently.

Energy Dispatch for CCHP System in Summer Based on Deep Reinforcement Learning

Combined cooling, heating, and power (CCHP) system is an effective solution to solve energy and environmental problems. However, due to the demand-side load uncertainty, load-prediction error, environmental change, and demand charge, the energy dispatch optimization of the CCHP system is definitely a tough challenge. In view of this, this paper proposes a dispatch method based on the deep reinforcement learning (DRL) algorithm, DoubleDQN, to generate an optimal dispatch strategy for the CCHP system in the summer. By integrating DRL, this method does not require any prediction information, and can adapt to the load uncertainty. The simulation result shows that compared with strategies based on benchmark policies and DQN, the proposed dispatch strategy not only well preserves the thermal comfort, but also reduces the total intra-month cost by 0.13~31.32%, of which the demand charge is reduced by 2.19~46.57%. In addition, this method is proven to have the potential to be applied in the real world by testing under extended scenarios.

Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services

Well-managed demand-side flexibility can effectively alleviate the stress of power systems caused by the increasing renewable energy penetration. The development of smart grid technologies provides win-win opportunities for system operators and demand-side users. Buildings have great flexibility potential through smart control of flexible loads, passive thermal mass storage and distributed active storage. However, a few fundamental problems on flexibility quantification, optimal coordination and profits sharing are faced when aggregating buildings to reach sufficient size for biding multiple flexibility services in electricity markets. Therefore, this paper proposes a multi-level optimal dispatch strategy and profit-sharing mechanism for non-residential building clusters to maximize flexibility profits and reduce the complexity of the optimization. At building level, a model-based quantification method is developed to evaluate flexibility potential of multiple resources. At cluster level, the coordinated bidding strategy of the aggregator is proposed, and flexibility profits are distributed based on the cooperative game theory at the post-market stage. Test results show that the proposed dispatch strategy can reduce up to 20.4% of daily electricity cost at cluster level and ensure profit increase of individual buildings with different flexibility characteristics. The active participation of building clusters in electricity markets could reduce the use of costly backup generators.

A Low-Carbon Dispatch Strategy for Power Systems Considering Flexible Demand Response and Energy Storage

The consumption of traditional fossil energy brings inevitable environmental protection problems, which also makes the low-carbon transition in industrial development imminent. In the process of low-carbon transition, the power industry plays a very important role. However, the large-scale integration of renewable energy resources such as wind power and photovoltaic brings new characteristics to power system dispatch. How to design a dispatch strategy that considers both low-carbon demand and economic cost has become a major concern in power systems. The flexible resources such as demand response (DR) and energy storage (ES) can cooperate with these renewable energy resources, promoting the renewable energy generation and low-carbon process. Thus, a low-carbon dispatch strategy for power systems considering flexible DR and ES is proposed in this article. First, models of DR and ES based on their behavior characteristics are established. Then, a carbon emission index is presented according to China’s Clean Development Mechanism (CDM). Finally, the low-carbon dispatch strategy for power systems is proposed through the combination of the carbon emission index and flexible resource dispatch models. The simulation results show that the proposed dispatch strategy can significantly improve wind power consumption and reduce carbon emission.

Multisource Energy Storage System Optimal Dispatch Among Electricity Hydrogen and Heat Networks From the Energy Storage Operator Prospect

A multisource energy storage system (MESS) among electricity, hydrogen and heat networks from the energy storage operator's prospect is proposed in this article. First, the framework and device model of MESS is established. On this basis, a multiobjective optimal dispatch strategy of MESS is proposed. Considering the influence of time-of-use price, our dispatch strategy can achieve the unified effect of economy, efficiency, and stationarity. Ultimately, the proposed dispatch strategy is applied to a MESS to study the characteristic of the economic benefits, the influence of energy loss, and power fluctuation, etc. The results show that the MESS can be profitable through our optimized dispatch strategy. And the sale of hydrogen and heat can become the main income of the MESS. Furthermore, we find energy loss is the main challenge for MESS development and application. Considering the economy and efficiency of the system, the proposed dispatch strategy can achieve the optimal comprehensive benefits from the energy storage operator's prospect and support the sustainable development of the MESS.

Automatic Generation Control in Modern Power Systems with Wind Power and Electric Vehicles

The modern power system is characterized by the massive integration of renewables, especially wind power. The intermittent nature of wind poses serious concerns for the system operator owing to the inaccuracies in wind power forecasting. Forecasting errors require more balancing power for maintaining frequency within the nominal range. These services are now offered through conventional power plants that not only increase the operational cost but also adversely affect the environment. The modern power system emphasizes the massive penetration of wind power that will replace conventional power plants and thereby impact the provision of system services from conventional power plants. Therefore, there is an emergent need to find new control and balancing solutions, such as regulation reserves from wind power plants and electric vehicles, without trading off their natural behaviors. This work proposes real-time optimized dispatch strategies for automatic generation control (AGC) to utilize wind power and the storage capacity of electric vehicles for the active power balancing services of the grid. The proposed dispatch strategies enable the AGC to appropriately allocate the regulating reserves from wind power plants and electric vehicles, considering their operational constraints. Simulations are performed in DIgSILENT software by developing a power system AGC model integrating the generating units and an EVA model. The inputs for generating units are considered by selecting a particular day of the year 2020, when wind power plants are generating high power. Different coordinated dispatch strategies are proposed for the AGC model to incorporate the reserve power from wind power plants and EVs. The performance of the proposed dispatch strategies is accessed and discussed by obtaining responses of the generating units and EVs during the AGC operation to counter the initial power imbalances in the network. The results reveal that integration of wind power and electric vehicles alongside thermal power plants can effectively reduce real-time power imbalances acquainted in power systems due to massive penetration of wind power that subsequently improves the power system security. Moreover, the proposed dispatch strategy reduces the operational cost of the system by allowing the conventional power plant to operate at their lower limits and therefore utilizes minimum reserves for the active power balancing services.

Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning

Dynamic energy dispatch is an integral part of the operation optimization of integrated energy systems (IESs). Most existing dynamic dispatch schemes depend heavily on explicit forecast or mathematical models of the future uncertainties. Due to the randomness of renewable energy generation and energy demands, these approaches are limited by the accuracy of forecasting or model. A novel model-free dynamic dispatch strategy for IES based on improved deep reinforcement learning (DRL) is proposed to solve the problem. The IES dynamic dispatch problem is formulated as a Markov decision process (MDP), in which the uncertainties of renewable generation, electric load and heat load are considered. For solving the MDP, an improved deep deterministic policy gradient (DDPG) algorithm using prioritized experience replay mechanism and L2 regularization is developed, so as to improve the policy quality and learning efficiency of the dispatch strategy. The proposed approach does not require any forecast information or distribution knowledge, and can adaptively respond to the stochastic fluctuations of the supply and demands. Simulation results show the proposed dispatch strategy has faster convergence and lower operating costs than original DDPG-based strategy. In addition, the advantages of the proposed approach in terms of cost-effectiveness and stochastic environmental adaptation are validated.

Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization

The electric vehicle to grid (V2G) interaction technology can improve the utilization of renewable energy and stabilize its grid connection. At the same time, renewable energy can be used for a microgrid nearby, or incorporated into a large grid, to effectively address the volatility of renewable energy sources. Motivated by the increasing number of electric vehicles (EVs) and the randomness of renewable energy output, this paper proposes an effective strategy to improve the security and economy of the microgrid system. The uncertainty of wind power and EV’s state of charge (SOC) is modeled as uncertainty prediction sets. And considering the worst-case scenario, this proposed strategy can increase the absorption ratio of renewable energy while orderly guiding the charging and discharging of EVs in peak-load reduction and valley filling and thus, lower operating costs under various practical constraints. To solve the problem of over-conservatism of the robust optimization, this paper introduces a dispatch interval coefficient to adjust the degree of conservatism, while improving the economy of microgrids system. The robustness and feasibility of the proposed dispatch strategy are demonstrated by numerical case studies.

Gen-Set Control in Stand-Alone/RES Integrated Power Systems

Power supply in stand-alone power systems, such those in non-grid connected islands, represents an important area of study in investigating smart energy concepts. In particular, the discontinuity in renewable energy availability and the mismatch with power demand are likely to hinder grid stability and overall system efficiency. Typically, the load-levelling relies on diesel engine gen-sets which suffer the modulation of power output resulting in increased operation costs and life time reduction. Although energy storage can complement power fluxes balance, a proper dispatch strategy is needed in order to improve diesel engine operations in Renewable Energy Source (RES) integrated systems. The present study investigates the merit of a dispatch strategy aiming at improving gen-set performance in a hybrid RES/storage/Diesel Engine Generator set (DEGS) power configuration. The proposed dispatch strategy is modelled in a transient simulation software, with hourly based analysis over a year period and is applied to a small island case study.

Two-stage Coordinated and Dynamic Economic Dispatch of Multi-microgrid System Considering Hierarchical Structure

Multi-microgrid (MMG) system consists of several microgrids (MGs) close to each other in space, and is more resilient to uncertainties and economical in operation than single MG. To coordinate the different goals of MMG system and individual MGs, a hierarchical dispatch structure is firstly established in this paper. Based on this, a two-stage dynamic economic dispatch (DED) scheme is then proposed to achieve the compromise between global and local scope. In Stage I, MMG system’s operation cost is minimized to determine the power exchange among MGs, while in Stage II, each MG’s purchased power from main grid is optimized to realize local economy. Finally, numerical tests are performed on a triple-MG system and different cases are studied to demonstrate the effectiveness and feasibility of the proposed dispatch strategy.

Coordinated Dispatch of Virtual Energy Storage Systems in Smart Distribution Networks for Loading Management

The growth in residential air-conditioning is a primary contributor to electric utility critical peak load causing millions of dollars spent on extra network infrastructure to cater for these peak times. This paper aims to provide an attempt to coordinate multiple groups of aggregated air-conditioners for distribution network loading management. Through limited communication to exchange information among neighboring aggregators, the proposed dispatch strategy shares the required active power curtailment among aggregators, maintaining room temperatures to keep occupants comfort in the meanwhile. Three case studies and sensitivity analysis are conducted to show the performance of the proposed scheme. The results show that it can provide technical and economic benefits to both participating residents and network operators.

Sample Entropy Based Net Load Tracing Dispatch of New Energy Power System

The high-proportion of renewable energies is gradually becoming one of the main power supply sources and bringing strong uncertainties to the power grid. In this paper, a sample entropy (SampEn) based net load tracing dispatch strategy with a specific thermal generating mode is proposed. In this strategy, renewable energies are fully and preferentially consumed by electric loads, turned to net loads, to maximize the utilization of renewable energies. SampEn theory is utilized to evaluate the complexity of net load time series, based on which, the traditional power generators trace the complexity of the net load flexibly. According to the SampEn, a specific generating model of thermal generators is determined and the cooperation between thermal generators and pumped storage is realized, aiming at reducing the ramp power of thermal generators and increasing the throughput of pumped storage. The experiment simulation is developed on the 10-unit test system. Results show that the ramping power of the thermal generators are reduced 43% and 13% in the two cases together with the throughput of pumped storage is increased 44% and 27% on the premise that the economy of the system is maintained and renewable energies are fully consumed. Therefore, the efficiency and reasonability of the proposed dispatch strategy are confirmed.

Sizing Energy Storage Based on a Life-Cycle Saving Dispatch Strategy to Support Frequency Stability of an Isolated System With Wind Farms

Wind power fluctuations usually cause grid frequency deviations, which could have a significant impact on an isolated power system. This paper proposes a dispatch strategy of the energy storage (ES) to support grid frequency stability of the isolated power system. The dispatch strategy of the ES is based on the forecasted mean wind speed and the corresponding standard deviation. Based on the dispatching scheme, a power spectrum density (PSD)-based method has been developed for sizing the ES and validated through system simulations based on the actual wind speed data and real isolated system in China. Results presented in this paper have shown demonstrated that the cycle-life of the ES can be significantly saved by using the proposed dispatch strategy. The developed ES sizing method has taken the random characteristics of wind power fluctuations and the frequency responses of the system into full consideration. Therefore, the proposed dispatch strategy and sizing method of the ES is general and can be applied to other isolated power systems with the ES being used to support grid frequency stability.

Operational Flexibility Constrained Intraday Rolling Dispatch Strategy for CHP Microgrid

The increasing penetration of variable renewable energy sources (RES) such as wind power and photovoltaic cell in the microgrid poses a great threat to the power balance. The microgrid operator has to consider the system operational flexibility in the dispatch to overcome the uncertainties. For a multi-energy microgrid, the coupling relationship and complementarity of different energy provides potential capacity for operational flexibility. In this paper, we propose an operational flexibility metric to quantify the ability of the combined heat and power (CHP) microgrid to overcome RES uncertainties in operation. An intraday rolling dispatch strategy for the off-grid CHP microgrid is proposed, which consists of a dispatch decision (DD) stage and a real-time adjustment (RTA) stage. The operational flexibility constraints are incorporated in the DD model to ensure that the uncertainties in the real-time operation can be suppressed. The RTA model is based on a linear programming model. Case studies demonstrate that the proposed dispatch strategy can overcome the uncertainties efficiently and ensure the secure and economic operation of the system.

Adjustable Robust Optimization Algorithm for Residential Microgrid Multi-Dispatch Strategy with Consideration of Wind Power and Electric Vehicles

A prospect of increasing penetration of uncoordinated electric vehicles (EVs) together with intermittent renewable energy generation in microgrid systems has motivated us to explore an effective strategy for safe and economic operation of such distributed generation systems. This paper presents a robust economic dispatch strategy for grid-connected microgrids. Uncertainty from wind power and EV charging loads is modeled as an uncertain set of interval predictions. Considering the worst case scenario, the proposed strategy can help to regulate the EV charging behaviors, and distributed generation in order to reduce operation cost under practical constraints. To address the issue of over-conservatism of robust optimization, a dispatch interval coefficient is introduced to adjust the level of robustness with probabilistic bounds on constraints, which gradually improves the system's economic efficiency. In addition, in order to facilitate the decision-making strategies from an economic perspective, this paper explores the relationship between the volatility of uncertain parameters and the economy based on the theory of interval forecast. Numerical case studies demonstrate the feasibility and robustness of the proposed dispatch strategy.

Multi-dispatch for Grid-connected Microgrid with Robust Optimization Algorithm

Abstract Increasing penetration of uncoordinated electric vehicles (EVs) and intermittent renewable energy generation in microgrid systems motivates us to explore an effective strategy for the safe and economic operation of such distributed generation (DG) systems. This paper presents a robust economic dispatch strategy for grid-connected microgrids. Uncertainty from wind power and EV charging loads is considered as an uncertain set of interval predictions. Considering the worst application scenario, the proposed strategy can help to schedule the EV charging behaviors and DG outputs in order to reduce operation cost under practical constraints. In addition, in order to facilitate the decision makers to make decision strategies from the perspective of economy, this paper explores the relationship between the volatility of uncertain parameters and economy based on the theory of interval forecast. Numerical case study shows the feasibility of the proposed dispatch strategy.