Dispatch Instructions Research Articles

Distinguishing the energy and non-energy actions in balancing energy markets

In the European context, balancing energy markets are established to optimise transmission system operator balancing actions closer to real-time. These actions aim to match total generation and consumption subject to a suite of security constraints (e.g., reserve requirements). However, there is no clear border between those actions that are taken due to the reserve requirements (non-energy actions) and those that are primarily taken to supply the demand mismatches (energy actions). To recognise the effect of non-energy actions, existing methods require comparing the results of counterfactual optimisation problems in which the non-energy-action-related constraints were deliberately omitted. This paper proposes a one-off solution enabling TSOs to distinguish energy actions from non-energy ones in the balancing market scheduling problem. By decomposition of the dual variables and clustering the constraints as proposed in this paper, there is no need to solve repetitive counterfactual optimisation problems. Case studies show that in addition to the non-energy actions caused by non-energy-based balancing requirements, the proposed method is able to recognise the energy actions that should be taken due to the non-energy root causes. This feature enables TSOs to efficiently retrace the effect of non-energy actions on the energy-based dispatch instructions issued according to the balancing market schedule.

Reservoir Optimization Scheduling Driven by Knowledge Graphs

As global climate change intensifies, the challenges of water scarcity and flood disasters become increasingly severe. This severity makes efficient reservoir scheduling management crucial for the rational utilization of water resources. Due to the diverse topological structures and varying objectives of different watersheds, existing optimization models and algorithms are typically applicable only to specific watershed environments. This specificity results in a “one watershed, one model” limitation. Consequently, optimization of different watersheds usually requires manual reconstruction of models and algorithms. This process is not only time-consuming but also limits the versatility and flexibility of the algorithms. To address this issue, this paper proposes a knowledge graph-driven method for reservoir optimization scheduling. By improving genetic algorithms, this method allows for the automatic construction of optimization models tailored to specific watershed characteristics based on knowledge graphs. This approach reduces the dependency of the optimization model on manual modeling. It also integrates hydrodynamic simulations within the watershed to ensure the effectiveness and practicality of the genetic algorithms. Furthermore, this paper has developed an algorithm that directly converts optimized reservoir outflow into actionable dispatch instructions. This method has been applied in the Pihe River Basin, optimizing flood control and resource management strategies according to different seasonal demands. It demonstrates high flexibility and effectiveness under varying hydrological conditions, significantly enhancing the operational efficiency of reservoir management.

Impact of drone-specific dispatch instructions on the safety and efficacy of drone-delivered emergency medical treatments: A randomized simulation pilot study

IntroductionMedical drones have potential for improving the response times to out-of-hospital emergencies. However, widespread adoption is hindered by unanswered questions surrounding medical dispatch and bystander safety. This study evaluated the impact of novel drone-specific dispatch instructions (DSDI) on bystanders’ ability to interact effectively with a medical drone and provide prompt, safe, and high-quality treatment in a simulated emergency scenario.11Drone-specific dispatch instructions. We hypothesized DSDI would improve bystanders’ performance and facilitate safer bystander-drone interactions. MethodsTwenty-four volunteers were randomized to receive either DSDI and standard Medical Priority Dispatch (MPD) instructions or MPD alone in a simulated out-of-hospital cardiac arrest (OHCA) or pediatric anaphylaxis.,22Medical priority dispatch3 Out-of-hospital cardiac arrest.3 Participants in the DSDI group received detailed instructions on locating and interacting with the drone and its enclosed medical kit. The simulations were video recorded. Participants completed a semi-structured interview and survey. ResultsThe addition of DSDI did not lead to statistically significant changes to the overall time to provide care in either the anaphylaxis or OHCA simulations. However, DSDI did have an impact on bystander safety. In the MPD only group, 50% (6/12) of participants ignored the audio and visual safety cues from the drone instead of waiting for it to be declared safe compared to no DSDI participants ignoring these safety cues. ConclusionsAll participants successfully provided patient care. However, this study indicates that DSDI may be useful to ensure bystander safety and should be incorporated in the continued development of emergency medical drones.

Performance of the Medical Priority Dispatch System® in Identifying Patients Requiring Chest Compressions at Overdose Prevention Services: A Retrospective Cohort Study

Background and Aims The Medical Priority Dispatch System (MPDS)® is used to triage 9-1-1 calls according to acuity, with certain coding receiving telecommunicator cardiopulmonary resuscitation (T-CPR) for suspected out-of-hospital cardiac arrest (OHCA). However, this may be challenging for those with drug poisoning emergencies, who may resemble OHCA. We sought to examine the performance of the system to correctly identify cases requiring T-CPR, specifically at overdose prevention services (OPS). Methods This retrospective cohort study included patients attended by the provincial emergency medical system (EMS) (May 1, 2019–January 31, 2023). We calculated the diagnostic performance of MPDS® assessment of whether the case required T-CPR instructions against the gold standard of whether the patient was found pulseless on EMS clinician arrival. We compared performance among subgroups, specifically OPS vs other locations and drug poisoning-classified cases vs other case classifications. Results Comparing OPS to other locations, the sensitivity of MPDS® was similar (66.7% vs 62.4%, p = 0.4), with lower specificity (87.3% vs 98.1%, p < 0.01) and positive predictive value (0.3% vs 35.7%, p < 0.01) and higher negative predictive value (99.9% vs 99.4%, p < 0.01). The negative likelihood ratio of MPDS® was 0.381 at OPS locations, compared with 0.383 at other locations, while the positive likelihood ratio was 5.24, compared with 32.36. In patients with drug poisoning emergencies, compared with other 9-1-1 events, MPDS® had higher sensitivity (83.6% vs 60.6%, p < 0.01) but lower specificity (77.6% vs 98.9%, p < 0.01) and positive predictive value (10.5% vs 48.5%, p < 0.01), and similar negative predictive value (99.33% vs 99.35%, p = 0.03). The negative likelihood ratio of MPDS® was 0.212 in drug poisoning emergencies compared with 0.398 for all other presentations, and the positive likelihood ratio was 3.73 compared with 57.88. Discussion and Conclusions The ability of MPDS® to correctly identify patients needing telecommunicator cardiopulmonary resuscitation instructions differed between OPS settings and other locations, frequently recommending T-CPR for patients not suffering OHCA at an OPS. Different strategies developed in collaboration with people who use substances are required to better tailor dispatch instructions prior to EMS arrival to avoid delays in life-saving interventions.

Distributed Energy Resource and Energy Storage Investment for Enhancing Flexibility Under a TSO-DSO Coordination Framework

This paper presents a distributed energy resource and energy storage investment method under a coordination framework between transmission system operators (TSOs) and distribution system operators (DSOs), which simultaneously addresses two main aspects of the flexibility aggregation of DSOs, i.e., flexibility enhancement and dynamic flexibility provision. First, to characterize the key flexibility features of power distribution networks, a multi-port multi-period feasible region formulation is designed using the robust optimization conception while avoiding over-parameterized formulations that require extensive information exchange. Second, a two-stage robust planning model is built to enhance distribution system flexibility by maximizing the volume of feasible regions, in which the uncertainty of dispatch instructions is modeled as a decision-dependent uncertainty (DDU) set and the worst realization is identified for the operational security evaluation. The proposed two-stage robust planning model is solved via a customized column-and-constraint generation algorithm. Finally, a distributed framework for TSO-DSO coordination is proposed to enable the dynamic adjustment of feasible region provision of DSO, given the TSO’s preference, which is then solved by a DDU-based two-stage robust extension of the alternating direction method of multipliers algorithm. Numerical results verify the effectiveness of our proposed models and the scalability of the associated algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The increasing integration of renewable energy resources has stimulated the need for aggregated flexibility provided by power distribution networks (PDNs). However, there are three challenges: i) using the currently feasible region formulation of PDN requires extensive information exchange with the transmission system operator (TSO), thus not compatible with the current structure. ii) the optimal investment of various assets to leverage their cost-effectiveness in feasible region enhancement needs to be addressed. iii) lack of a proper coordination mechanism between TSO and distribution system operators (DSOs) to facilitate the optimal feasible region provision and leverage the spatiotemporal flexibility. To address the above challenges, we design an easy-to-implement feasible region formulation to characterize the key flexibility features of PDNs. Upon this, we propose a feasible region enhancement planning method based on an extended two-stage robust model to address the decision-dependent uncertainty. Furthermore, we facilitate the optimal feasible region provision through the TSO-DSO coordination process in a distributed manner. The numerical results show that the proposed planning method and coordination mechanism could effectively achieve optimal flexibility enhancement and dynamic flexibility provision. In practical application, our proposed planning method can be readily implemented with advanced analytical tools. The coordination mechanism is also compatible with the current TSO-DSO structure and can be easily integrated into it.

Correlated Equilibrium Power Flow in Distribution Networks Using Graphical Game Theory

Modern power systems necessitate active management of distribution networks by Distribution System Operators (DSOs). A DSO can harness the flexibility capabilities of the nodes towards ensuring the safe operation of the distribution system, while optimizing a certain system objective. The objective-optimizing dispatch can be obtained by solving the Optimal Power Flow (OPF) problem. However, selfish nodes may deviate from the dispatch instruction by calculating a profitable deviation. Thus, an implementable solution needs to be not only optimal, but also an equilibrium, i.e., a point from which no node is willing to deviate. In this paper, we propose the adoption of correlated equilibrium as a solution concept that is generally more efficient than Nash and more relevant to this setting where the DSO acts as a coordinating entity. We formulate the problem of finding an efficient correlated equilibrium for distribution networks with discrete resources. The problem’s complexity is managed by exploiting the graphical structure and sparse connectivity of distribution networks, drawing on the methodology of graphical games. Simulations showcase that the proposed approach achieves an equilibrium with near-optimal efficiency, in contrast to the standard OPF approach which is shown to be unstable when each node selfishly optimizes its own objective.

A Method for Calculating Aggregate Flexibility Parameters of Virtual Power Plant Based on Improved Column and Constraint Generation Algorithm

The virtual power plant (VPP) can aggregate massive, dispersed, and heterogeneous distributed energy resources (DERs) and provide an effective energy management way for an active distribution system (ADS). In this paper, three parameters of upper and lower limits of active power output, ramping, and discharging energy is used to describe the aggregate flexibility of VPP. Based on this, a two-stage robust optimization (RO) model with dispatch instructions (DI) from the superior power grid (SPG) as uncertainty variables was constructed. Considering that the range of DI from the SPG changes during the iteration of the column and constraint generation (C&CG) algorithm, a position set is proposed to record the boundary information of the uncertain set of DI issued by the SPG when the sub-problem (SP) finds the worst scenario in each iteration, and the corresponding constraints are added to the master problem (MP). For the solution of SP, the bilevel max-min model is firstly transformed into a single-level bilinear programming (BLP) model by the duality theory and then solved by the concave-convex process (CCP) iterative method. Compared with transforming the BLP problem to a mixed integer programming (MILP) problem based on Karush-Kuhn-Tucker (KKT) condition, it can not only meet the engineering accuracy but also greatly save the solution time. Finally, the effectiveness of the proposed model and algorithm was verified on a modified IEEE 33-bus system.

Multi-interval settlement system of rolling-horizon scheduling for electricity spot market

Multi-interval settlement is conducive to meeting the needs of growing renewable energy resources with great intermittency and volatility and managing the effective operation in the electricity spot market. However, the insufficient incentive of market price inaccurately reflecting the total cost of the electricity spot market caused by the inaccuracy of generation and load prediction in the current multi-interval settlement will lead to inefficient market scheduling, causing the market participants to deviate from dispatch instructions. Based on the problem above, a new multi-interval settlement system of rolling-horizon scheduling including the period selection of look-ahead schedules and enhanced settlement mechanism is proposed to improve the price incentive for the electricity spot market. The proposed multi-interval settlement system can produce a better look-ahead period and a more economically efficient dispatch solution inducing dispatch-following incentives. A numerical example shows that the proposed multi-interval settlement system outperforms the traditional settlement mechanism regarding economic efficiency.

A hierarchical dispatch strategy of hybrid energy storage system in internet data center with model predictive control

The internet data center (IDC) can improve the stability of power system and increase the utilization of uninterruptible power supply (UPS) with battery energy storage system (BESS) and hydrogen fuel cell (HFC) by participating in dispatch operations. This paper proposes a hierarchical dispatch strategy assisted by model predictive control (MPC) for UPS in IDC including available energy analysis, the upper-level power system dispatch strategy and the lower-level IDC dispatch strategy. The data security can be ensured through available energy analysis based on load forecasting information of IDC. Then, the upper-level dispatch model aims at increasing the utilization of UPS and the revenue of IDC via minimizing the total generation cost. Meanwhile, the lower-level dispatch method with MPC reallocates the upper-level dispatch instructions between BESS and HFC by balancing power tracking error and the state of charge of BESS. Finally, the effectiveness of the strategy is verified by several experiments in MATLAB platform. Numerical results show that the proposed strategy increases the utilization rate of the UPS by about 85% and the revenue by about ¥5950, and the SOC fluctuation is reduced to 12%-19%.

PV array reconfiguration with electrical energy storage system for power system frequency regulation

In this work, a novel PV station participating FR technique based on PV array reconfiguration and battery energy storage system (BESS) is put forward. Through the PV array reconfiguration under Partial shading condition, photovoltaic (PV) system can adjust the output power according to the power dispatch instruction. And with the help of BESS, The PV station can achieve continuous power output. Specifically, a joint PV energy storage system model combining profit maximization and power deviation minimization is established. In order to two conflicting goals simultaneously, multi-objective golden eagle optimizer and improved ideal point decision making method are applied. And Lagrange function is given to determine the weight coefficients of each objective more fairly. Two cases are designed and tested. In the high frequency response simulation test, the profit is increased by 8.4% and the power deviation from FR signal is decreased by 19.98%; meanwhile the profit is increased by 10.07% and power deviation is decreased by 67.69% in the low frequency response test. Therefore, the simulation results verify that the proposed method contributes to the frequency response of PV station.

Distributed Active Power Optimal Dispatching of Wind Farm Cluster Considering Wind Power Uncertainty

With the large-scale volatility and uncertainty of the centralized grid connection of wind power, the dimensionality disaster problem of wind farm cluster (WFC) scheduling optimization calculation becomes more and more significant. In view of these challenges, a distributed active power optimal dispatch model for WFC based on the alternating direction multiplier method (ADMM) is proposed, and the analytical description of the distribution characteristics of the active power output of wind farms is introduced into the proposed model. Firstly, based on the wake effect, the Weibull distribution of wind speed is transformed by the impulse function to establish an analytical model of the active output distribution of the wind farm. Secondly, the optimization goal is to minimize the expected sum of the deviations of the dispatch instructions and the output probability density function of each wind farm, constructing a WFC active power optimal dispatch model considering uncertainty. Finally, the proposed model is decoupled in space and time into sub-optimization problems, and the ADMM is improved to construct an efficient solution algorithm that can iterate in parallel and decouple a large number of decision variables at the same time. The model is compared with other current classical models through the evaluation of multiple recommendation evaluation metrics, and the experimental results show that the model has a 3–7% reduction in dispatched power shortfalls and a 4–21% improvement in wind power utilization. The optimization algorithm for model construction has extremely high computational efficiency and good convergence. The results show that when the update step size is three, the convergence is the fastest, and when the update step size is six, the convergence is the slowest; in addition, when the number of wind farms is greater than eight, the distributed computing efficiency has an incomparable advantage over the centralized one. It plays a helpful role in wind power consumption and the efficient calculation of the power grid and effectively improves the reliability of the power grid.

A multi-agent deep reinforcement learning approach enabled distributed energy management schedule for the coordinate control of multi-energy hub with gas, electricity, and freshwater

In recent years, due to the deeply concerns on environment protection, the production, transformation and utilization of the energy sources with a more efficient and various way has become an important research topic. Under this background, this paper designs a renewable energy powered multi-energy hub system with gas, electricity, and freshwater sub-system. To enhance the flexibility and reduce the total cost of such system, the energy management of the multi-energy hub is formed as multi-agent cooperative control, and several targets, including operational costs, environment cost, and maintenance cost are considered along with the constraints. Subsequently, a novel attention mechanism-based multi-agent deep reinforcement learning algorithm is applied, where multi-agents are centrally trained to obtain the coordinate energy management strategy while being executed in a decentralized manner to provide the dispatch instruction for each energy hub with only local states. Finally, the effectiveness of the proposed method is investigated in the study system and the simulation results show that it can reduce the total cost by up to 7.28% compared with the other benchmark methods.

Attitude and behavior toward bystander cardiopulmonary resuscitation during COVID-19 outbreak.

BackgroundOutbreaks of emerging infectious diseases, such as COVID-19, have negative impacts on bystander cardiopulmonary resuscitation (BCPR) for fear of transmission while breaking social distancing rules. The latest guidelines recommend hands-only cardiopulmonary resuscitation (CPR) and facemask use. However, public willingness in this setup remains unknown.MethodsA cross-sectional, unrestricted volunteer Internet survey was conducted to assess individuals’ attitudes and behaviors toward performing BCPR, pre-existing CPR training, occupational identity, age group, and gender. The raking method for weights and a regression analysis for the predictors of willingness were performed.ResultsAmong 1,347 eligible respondents, 822 (61%) had negative attitudes toward performing BCPR. Healthcare providers (HCPs) and those with pre-existing CPR training had fewer negative attitudes (p < 0.001); HCPs and those with pre-existing CPR training and unchanged attitude showed more positive behaviors toward BCPR (p < 0.001). Further, 9.7% of the respondents would absolutely refuse to perform BCPR. In contrast, 16.9% would perform BCPR directly despite the outbreak. Approximately 9.9% would perform it if they were instructed, 23.5%, if they wore facemasks, and 40.1%, if they were to perform hands-only CPR. Interestingly, among the 822 respondents with negative attitudes, over 85% still tended to perform BCPR in the abovementioned situations. The weighted analysis showed similar results. The adjusted predictors for lower negative attitudes toward BCPR were younger age, being a man, and being an HCP; those for more positive behaviors were younger age and being an HCP.ConclusionsOutbreaks of emerging infectious diseases, such as COVID-19, have negative impacts on attitudes and behaviors toward BCPR. Younger individuals, men, HCPs, and those with pre-existing CPR training tended to show fewer negative attitudes and behaviors. Meanwhile, most individuals with negative attitudes still expressed positive behaviors under safer measures such as facemask protection, hands-only CPR, and available dispatch instructions.

Optimal multi-time scale coordination method for electricity-heating integrated energy system scheduling

Considering that different energy subsystems have different control characteristics in equipment operations, an optimal multi-time scale coordination method for electricity-heating integrated system scheduling is proposed. It merges three optimal scheduling models based on three-time scales of day-ahead, rolling, and real-time. The coordinated optimization of electricity and heating energies with the same dispatch instruction is fulfilled in day-ahead scheduling. And all feasible scheduling instruction cycles are traversed in the rolling stage to improve the stability of system economic operation. The calculation examples show that the proposed overall strategy for multi-time scale scheduling can achieve multi-energy complementarity, and the optimization results have less deviation from the actual load.

Analysis of short-term operational forecast deviations and controllability of utility-scale photovoltaic plants

The growing deployment of utility-scale photovoltaic (PV) in electricity industries raises many concerns for power system operation. Due to the highly variable and somewhat unpredictable characteristics of short-term PV output, the system operator faces challenges in balancing electricity supply and demand, which increases the need for short-term frequency management. Although many studies have been carried out to enhance the understanding of short-term PV output characteristics, the uncertainty around forecast targets for electricity market dispatch has not been studied. This paper analyses 4-s SCADA output data from PV plants and all other generators operating in the Australian National Electricity Market (NEM) to assess the deviation from their forecast dispatch targets. During the study period, PV penetration was less than 5% of the total generation capacity. The deviations of PV plants from forecast targets increases almost proportionally with their operating level. The correlation between deviations from the targets and system frequency deviations shows that PV plants contribute towards increasing frequency deviations, yet the impact seems minor, with only 0.06–0.09 correlation coefficients. Despite their variability during normal operation, PV plants demonstrate accurate output control when following dispatch instructions during network constrained periods with fluctuations of only ±1% of the plant rated capacity.

Coordination trial of novel distributed energy resources management system to provide reactive power services to address transmission constraints

This study presents the end-to-end design solution of a novel distributed energy resource management system (DERMS) to provide reactive power services to the transmission network from distributed energy resources. These studies are part of the pre-commissioning tests before commencing the wave 1 trial of the innovation project Power Potential which is a joint effort between UK Power Networks and National Grid Electricity System Operator. The system integration and all the interfaces are described, followed by a summary of the integration test scenarios to run the system under both normal and failure conditions. The simulation results of the lab-integration testing in a pre-production environment are also presented considering various scenarios which can trigger the reactive power dispatch instruction on DERMS.

Secure Communication Modeling for Microgrid Energy Management System: Development and Application

As the number of active components increase, distribution networks become harder to control. Microgrids are proposed to divide large networks into smaller, more manageable portions. The benefits of using microgrids are multiple; the cost of installation is significantly smaller and renewable energy-based generators can be utilized at a small scale. Due to the intermittent and time dependent nature of renewables, to ensure reliable and continuous supply of energy, it is imperative to create a system that has several generators and storage systems. The way to achieve this is through an energy management system (EMS) that can coordinate all these generators with a storage system. Prior to on-site installation, validation studies should be performed on such controllers. This work presents a standardized communication modeling based on IEC 61850 that is developed for a commercial microgrid controller. Using commercial software, different terminals are set up as intelligent electronic devices (IEDs) and the operation of the EMS is emulated with proper message exchanges. Considering that these messages transmit sensitive information, such as financial transactions or dispatch instructions, securing them against cyber-attacks is very important. Therefore; message integrity, node authentication, and confidentiality features are also implemented according to IEC 62351 guidelines. Real-message exchanges are captured with and without these security features to validate secure operation of standard communication solution.

Optimal Dispatch of Aggregated HVAC Units for Demand Response: An Industry 4.0 Approach

Demand response (DR) involves economic incentives aimed at balancing energy demand during critical demand periods. In doing so DR offers the potential to assist with grid balancing, integrate renewable energy generation and improve energy network security. Buildings account for roughly 40% of global energy consumption. Therefore, the potential for DR using building stock offers a largely untapped resource. Heating, ventilation and air conditioning (HVAC) systems provide one of the largest possible sources for DR in buildings. However, coordinating the real-time aggregated response of multiple HVAC units across large numbers of buildings and stakeholders poses a challenging problem. Leveraging upon the concepts of Industry 4.0, this paper presents a large-scale decentralized discrete optimization framework to address this problem. Specifically, the paper first focuses upon the real-time dispatch problem for individual HVAC units in the presence of a tertiary DR program. The dispatch problem is formulated as a non-linear constrained predictive control problem, and an efficient dynamic programming (DP) algorithm with fixed memory and computation time overheads is developed for its efficient solution in real-time on individual HVAC units. Subsequently, in order to coordinate dispatch among multiple HVAC units in parallel by a DR aggregator, a flexible and efficient allocation/reallocation DP algorithm is developed to extract the cost-optimal solution and generate dispatch instructions for individual units. Accurate baselining at individual unit and aggregated levels for post-settlement is considered as an integrated component of the presented algorithms. A number of calibrated simulation studies and practical experimental tests are described to verify and illustrate the performance of the proposed schemes. The results illustrate that the distributed optimization algorithm enables a scalable, flexible solution helping to deliver the provision of aggregated tertiary DR for HVAC systems for both aggregators and individual customers. The paper concludes with a discussion of future work.

Pricing in Multi-Interval Real-Time Markets

This paper examines multi-interval real-time markets in the context of U.S. independent system operators (ISOs). We show that current ISO implementations that settle only the upcoming interval of the multi-interval solution can create incentive problems. Fundamentally, this is the result of each successive optimization problem treating historical losses as sunk costs. To solve the incentive issues, we present an existing proposal and a new multi-interval pricing method. Both methods incorporate historical losses and produce prices that appropriately incentivize generators to follow multi-interval dispatch instructions when implemented with a fixed finite horizon and perfect foresight. In practice, the fixed horizon requirement necessary for this result is not satisfied because new information about the future is constantly being revealed to the ISO as time proceeds. Realistic rolling horizon implementations for an ISO New England-based system suggest that the new method results in lower out-of-market payments than both current ISO implementations and the existing proposal.

A Novel Two-Stage Photovoltaic Grid-Connected Inverter Voltage-Type Control Method with Failure Zone Characteristics

This paper investigates how to develop a two-stage voltage-type grid-connected control method for renewable energy inverters that can make them simulate the characteristics of a synchronous generator governor. Firstly, the causes and necessities of the failure zone are analyzed, and thus the traditional static frequency characteristics are corrected. Then, a novel inverter control scheme with the governor’s failure zone characteristics is proposed. An enabling link and a power loop are designed for the inverter to compensate fluctuations and regulate frequency automatically. Outside the failure zone, the inverter participates in the primary frequency regulation by disabling the power loop. In the failure zone, the droop curve is dynamically moved to track the corrected static frequency characteristic by enabling the power loop, resisting the fluctuation of grid frequency. The direct current (DC) bus voltage loop is introduced into the droop control to stabilize the DC bus voltage. Moreover, the designed dispatch instruction interface ensures the schedulability of the renewable energy inverter. Finally, the feasibility and effectiveness of the proposed control method are verified by simulation results from MATLAB (R2016a).