On-line Dispatch Research Articles

Distributed sequential optimal power flow under uncertainty in power distribution systems: A data-driven approach

Modern distribution systems with high penetration of distributed energy resources face multiple sources of uncertainty. This transforms the traditional Optimal Power Flow problem into a problem of sequential decision-making under uncertainty. In this framework, the solution concept takes the form of a policy, i.e., a method of making dispatch decisions when presented with a real-time system state. Reasoning over the future uncertainty realization and the optimal online dispatch decisions is especially challenging when the number of resources increases and only a small dataset is available for the system’s random variables. In this paper, we present a data-driven distributed policy for making dispatch decisions online and under uncertainty. The policy is assisted by a Graph Neural Network but is constructed in such a way that the resulting dispatch is guaranteed to satisfy the system’s constraints. The proposed policy is experimentally shown to achieve a performance close to the optimal-in-hindsight solution, significantly outperforming state-of-the-art policies based on stochastic programming and plain machine-learning approaches.

Day-Ahead Scheduling and Online Dispatch of Energy Hubs: A Flexibility Envelope Approach

Day-Ahead Scheduling and Online Dispatch of Energy Hubs: A Flexibility Envelope Approach

A Data-Driven Method for Online Constructing Linear Power Flow Model

To adapt the data-driven linear power flow (DD-LPF) model for online dispatch in power systems, especially when the size of historical samples or system scale are relatively large, this paper proposes an online LPF model construction method. First, the raw measurements are filtered to suppress the adverse impact of measurement noise and outliers. Then, an adaptive moment estimation-based distributed stochastic gradient descent (Adam-DSGD) method is utilized to improve solution efficiency of DD-LPF parameters. Considering the features of online dispatch in power systems, a recursive forgetting factor (RFF) method is further introduced to adapt the LPF model for the present operating condition. Subsequently, the proposed method is applied to an online dispatch-command-tracking problem to demonstrate its practicability. Case studies confirm that the proposed method can achieve 2- to 100-fold less computing time and maintain satisfactory linearization accuracy simultaneously. Moreover, the tracking rate for the optimization objective is also enhanced with applying the proposed method to the real-time optimization problem.

Energy management for regional microgrids considering energy transmission of electric vehicles between microgrids

As the proliferation of electric vehicles (EVs) continues to accelerate, the inherent attributes of EVs warrant meticulous consideration in the realm of energy dispatch. In order to evaluate the ability of EVs as mobile energy storage, this paper presents an energy management framework for the microgrids' online dispatch, which accounts for the spatio-temporal energy transmission of EVs between microgrids. The energy management framework contains two iterative processes: optimizing charging price and guiding charging dispatch. To sufficiently capture the uncertainties of the renewable energy and load demand, chance-constrained optimization is utilized to determine the charging price by reasonable power allocation. To achieve a continuous and efficient control policy, a normalized advantage function-deep Q learning network (NAF-DQN) is developed for EV dispatch under V2G technology. The above two processes as a coupled optimization problem are solved alternately until convergence. Numerical cases considering energy transmission of EVs between microgrids are studied to demonstrate the superiority of the proposed dispatch framework. The simulation results indicate improved computational efficiency and higher-quality solution.

Data-Driven Combined Central and Distributed Volt/Var Control in Active Distribution Networks

This paper proposes a two-stage combined central and distributed Volt/Var control (VVC) strategy to coordinate both utility-scale and customer-owned photovoltaics (PVs) for voltage regulation in active distribution networks (ADN). In the first stage, with day-ahead PV and load predictions, the central controller (CC) performs optimal power flow (OPF) to determine the hourly dispatch of on-load tap changers (OLTC) and capacitor banks (CBs). In the second stage, the ADN is partitioned into different zones only based on PV and load types. The zone controllers (ZC), which is formulated by deep neural networks (DNN), then communicate with their neighbors and track the optimal total reactive power support of PVs in each zone. In the offline training process, an analytical derivation to minimize power losses is also provided that can help the input and output selection of DNN. To online dispatch reactive power for individual utility-scale and customer-owned PVs in corresponding zones, a multi-mode reactive power allocation strategy is proposed. Three different modes namely loss reduction mode (LRM), fair power sharing mode (FPM) and voltage security mode (VSM) are designed to meet varying operation requirements, which can mitigate voltage violations with fair participation for customers and reduced power losses for system operators. Numerical simulations are performed on the IEEE 33-bus distribution system to demonstrate the effectiveness of the proposed method in finding a trade-off between voltage security and loss minimization with fairness.

Dispatch-aware planning for feasible power system operation

Maintaining stable energy production with increasing penetration of variable renewable energy requires sufficient flexible generation resources and dispatch algorithms that accommodate renewables’ uncertainty. In this work, we study the feasibility properties of real-time economic dispatch (RTED) algorithms and establish fundamental limits on their performance. We propose a joint methodology for resource procurement and online economic dispatch with guaranteed feasibility. Our algorithm, Feasible Fixed Horizon Control (FFHC) is a regularized form of Receding Horizon Control (RHC) that balances exploitation of good near-term demand predictions with feasibility requirements. Empirical evaluation of FFHC in comparison to the standard RHC on realistic load profiles highlights that FFHC achieves near-optimal performance while ensuring feasibility in high-ramp scenarios where RHC becomes infeasible. • Increasing variability of net demand requires dispatch-aware power system planning. • Offline planning is insufficient to ensure online dispatch feasibility. • Feasible planning algorithm uses affine feedback policies to approximate dispatch. • Online economic dispatch algorithm adapts standard receding horizon control. • Joint algorithm achieves guaranteed feasibility with near-optimal dispatch efficiency.

An online dispatch approach for distributed integrated multi‐energy system considering non‐ideal communication conditions

The distributed integrated multi-energy system (DIMS) has many advantages in terms of the utilisation of renewable energy sources and clean energy. Operation strategies for the DIMS based on a real-time profile have been extensively studied. In a DIMS online optimisation problem, besides fluctuations in the renewable energy output and load, inconsistent time scales of the transport dynamics of different energy flows and non-ideal communication (involving communication uncertainty and latency) result in suboptimal operation in dispatch scheduling. An online multi-time-scale optimal operation strategy is proposed for the DIMS to respond to the above challenges, using a hybrid algorithm comprising a model predictive control method and distributed collaborative consensus algorithm (CCA). The approach is based on a hierarchy, comprising rolling optimisation and adjustment. A rolling optimisation is established to schedule operations according to the latest forecast and status information. The rolling dispatch is then adjusted according to the ultrashort-term adjustment using the CCA. Meanwhile, the effect of the information transmission environment on real-time scheduling is considered, and the robust CCA is improved for the implementation of strategies under non-ideal communication conditions. Case studies and results are presented and discussed to show the effectiveness of the proposed approach with the better matching between demand and supply.

A Three-Stage Multitimescale Framework for Online Dispatch in a Microgrid With EVs and Renewable Energy

To accomplish more practical charging dispatch for electric vehicles (EVs) and effectively consider the multiuncertainties of renewable energy and load demand, this article presents a three-stage multitimescale framework for online dispatch of a grid-connected microgrid. In the first stage, a reference scheduling model is proposed to obtain the referral plans with a sampling period of 30 min. Considering these uncertainties, the second stage proposes a real-time operation method based on stochastic model predictive control, with a sampling period of 10 min, to update the referral plans by minimizing the deviation of the real results from the referral results. To sufficiently capture the operations of EV charging, a rule-based energy management model for the EV charging station is designed in the third stage. The controller of the third stage runs every minute and allocates the charging station power to the available EVs while meeting the charging demand and technical constraints. Numerical simulations are used to investigate the effectiveness and superiority of the proposed dispatch framework. The simulation results show that the proposed framework reaches 98% of ideal performance achieved by off-line optimization.

Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station

To achieve carbon neutrality and meet the increased charging demand of electric vehicles, microgrids incorporating renewable energy and charging stations are considered one of the potential solutions. However, the inevitable uncertainties of renewable energy and load demand become a challenge for the online charging dispatch of the microgrid. Considering these uncertainties, this paper proposes a two-stage optimal framework for the online dispatch of a grid-connected DC microgrid. The first stage presents a power coordination model to obtain the schedule plans of the main grid, the energy storage unit and the charging station, where the combined robust and stochastic model predictive control approach with different granular models is developed to solve this problem and effectively deal with these uncertainties. In the second stage, the charging station allocation model is designed to determine the charging power for every EV, which takes into account the max-min fairness of the charging power. Numerical cases in the presence of uncertainties are studied to evaluate the proposed dispatch framework and the solving approaches. The simulation results show its superiority in both computational efficiency and operating cost.

A Comprehensive Decomposition Based Hierarchical Heuristic Control of Multimicrogrids

This paper proposes a novel decomposition based hierarchical heuristic control of multimicrogrids to maximize the utilization of renewable energy sources and load satisfaction. The proposed control is capable of providing comprehensive online dispatch of active and reactive powers in steady state modes of operation according to IEEE 2030.7 while keeping in view the restrictions of the participants (microsources and/or microgrids). The proposed solution is generalized to be adopted with respect to different types of participants and grid structures, modular with respect to the number of its participants and capable of protecting the privacy of its participants to maximum possible extent. The proposed heuristic control decomposes the control problem at four levels of hierarchy namely microsource, unit, microgrid and multimicrogrid level. All the distributed energy sources have their own microsource controllers and all the distributed energy sources of the same type are put under one unit controller. All the unit controllers are put under a microgrid controller and all the microgrid controllers are put under a multimicrogrid controller. Information flows from lower to upper levels while dispatch commands flow vice versa. This provides a comprehensive, generic, modular and secure control of a multimicrogrid system. Test cases are simulated to verify the validity of the proposed control scheme.

Tri-stage optimal dispatch for a microgrid in the presence of uncertainties introduced by EVs and PV

This paper proposes a novel tri-stage online dispatch framework that coordinates the charging behaviors of electrical vehicles (EVs) within an AC/DC hybrid microgrid in the presence of uncertainties. A day-ahead scheduling model is proposed as the first stage to optimize the total operational cost for the next day (24 h). In the second stage, a receding horizon manner is adopted to adjust the day-ahead scheduling results, which can compensate for unpredictable disturbances. Both the first and second stages are operated with a time scale of one hour, which is, however, insufficient in capturing the operations of EVs. Hence, the third stage is introduced with the stochastic model predictive control (SMPC) method in a time scale of 10 min to further consider uncertainties of load, PV, EVs. The real-world behavioral data of eight private EVs in Beijing are used to evaluate the performance of our dispatch model. The simulation results show that compared with some traditional online dispatch methods, the total operational cost of the proposed dispatch framework is significantly reduced.

Online Dispatch of Shared Energy Storage with Remote Renewable Generations

The sustained penetration of wind and solar generation is conducive to alleviating the energy crisis and environmental pollution. However, the finite capacity of transmission corridor limits the delivery of remote renewables and leads to curtailment. Energy storage plays an important role in renewables accommodation and improving equipment utilization, and shared energy storage can magnify the benefits through a temporal and spatial complementary. This paper proposes an online dispatch approach of energy storage shared by multiple renewable plants. The uncertainty and non-anticipativity issue are addressed via a two-stage decision-making process. The day-ahead scheduling determines the allowable energy and power levels of shared energy storage; the real-time online dispatch decides the wait-and-see strategies of charging and discharging power based on a single-period optimization model constrained by the pre-scheduled bounds. The case study verifies the effectiveness of the proposed method.

Knowledge base formation for automation of dispatch control over power systems of the mining and metallurgical complex

Purpose. The research is aimed at developing and introducing methods of knowledge extraction concerning online control over power systems under emergency modes and building smart complexes of automatizing managerial decision making based on incorporated ontological knowledge bases. Methodology. The authors use the calculated planned experiment method applied to building sensitivity matrices of controlled parameters of power systems in sensor points to controlled factors and introduction of sensitivity coefficients into knowledge bases. Findings. The research suggests methods for obtaining and building a knowledgebase of professional ontologies for online control over power system modes. The problem of calculating sensitivity of controlled parameters to controlling actions is solved. Calculation results for the emergency mode enable building impact functions and determining sensitivity matrix coefficients. The smart system knowledgebase is built to provide decision support for dispatch control over power system modes under standard and emergency conditions. There are obtained sets of mode data used as knowledgebase components enabling efficient assessment of the emergency mode rate and its dispatch correction. Besides calculation parameters of intensity of controlling actions, the knowledgebase also comprises linguistic concepts, facts and rules of instructive dispatch materials. A knowledge base has been built on the basis of a subset of the linguistic corpus of concepts for the professional area of emergency response in the power system. Originality. For the first time, there is suggested an approach to incorporating various linguistic knowledge forms represented by a single ontological model and numerical parameters of sensitivity of the power system mode to controlling actions into an integrated knowledgebase, which enables building effective smart systems of dispatch decision support and implementing them into the operating automatized dispatch control system. Practical value. The ontological knowledgebase of online dispatch control is built that enables realizing a software complex of a decision support system aimed at automatizing online dispatch control over standard and emergency modes of power systems. Application of the suggested approach to building the knowledgebase and its use with online dispatch personnels decision support enhance reliability and increase maximum accessible time of personnels non-stop work by 1.5 years with absolute accident elimination, thus providing a significant economic effect.

Distributed Online Dispatch For Microgrids Using Hierarchical Reinforcement Learning Embedded With Operation Knowledge

This paper considers the problem of distributed online economic dispatch (DOED) from sequential data using reinforcement learning. Learning operation behavior in high-dimension environments with constraints is a major challenge for the DOED of networked microgrids (MGs), where insufficient exploration disables agents to build complex policies. Therefore, this paper develops a hierarchical reinforcement learning (HRL) to handle the DOED problem, where radial basis function (RBF) approximation is incorporated to make policies in continuous space. Based on the hierarchical framework, the HRL algorithm increases the training efficiency and reduces computational cost. The online HRL achieves distributed selfadaption and better performance of real-time dispatch by a modest number of interacting variables. In addition, guided by domain knowledge, the HRL algorithm avoids onerous additional learning beyond feasible action space. In the case of an actual networked MGcluster in Qingdao with real operation data, simulation is conducted to verify that the proposed learning framework can reduce longterm operation costs and enhance operation stability. To explore the learning process, we also provide its convergence condition andanalyze the sensitivity of the learning parameters.

Sequential Online Dispatch in Design of Experiments for Single- and Multiple-Response Surrogate Modeling

As parallel computing becomes increasingly important in many real-world applications, a batch sequential experimental design (BSED), which adds a batch of computer experiments per iteration and runs these simulations in parallel, is gaining popularity in surrogate modeling. This article proposes sequential online dispatch in design of experiments (SODDE) for single- and multiple-response surrogate modeling when multiple processors work in parallel but not independently. The proposed method includes several unique features: 1) it works with any popular acquisition function to select a single new sample point at each iteration; 2) it minimizes the idle time of all processors; 3) it rapidly updates the surrogate model; and 4) it dynamically reconstructs the surrogate model when a simulation process aborts, minimizing the impact incurred by the abortion. The effectiveness of SODDE is evaluated in one mathematical example and one industrial problem. The latter problem considers blade design of horizontal axis wind turbines (HAWTs). The cost of finding blade geometry that results in the desired aerodynamic behavior of HAWTs is estimated for BSED and SODDE. Relative to BSED, SODDE reduces the costs by up to approximately 31%. Note to Practitioners —Computer simulation models are often used to represent the behavior of complex engineered systems, such as automobiles, aircraft, and wind turbines. Although running computer simulations allows analyzing the performance of these systems, the simulations can still be computationally costly (e.g., hours of runtime for numerically solving a large number of differential equations in one simulation), which makes the performance analysis task challenging. It is important to design an innovative simulation process that replaces the original expensive simulation models by cheap-to-build surrogate models, and at the same time, minimize the computational costs for building the surrogate models. The new method developed in this article allows experimental designers to run batch sequential iterations and run these iterations in parallel. This, in engineering practice, will help meet the need for long and complex experimental designs scenarios in a parallel computing environment, where the computer “up time” (or runtime) needs to be maximized to save time and ultimately, money. Cost analysis results from an industrial problem involving the inverse design of wind turbine blades suggest that the proposed method reduces the computational costs by up to approximately 31% over an existing method.

Emergency wind power plant re‐dispatching against transmission system cascading failures using reverse tracking of line power flow

Due to high wind power penetration into power system, synchronous generators may no longer be the dominant generation, which implicitly requires participation of wind power plants (WPPs) in the load-frequency control (LFC). Frequency, a common variable throughout the entire power system, is the key variable for LFC, which causes equal contribution of all distant and nearby WPPs in LFC. Power transfer from distant WPPs to the event location may overload the middle transmission lines leading to cascading failures. This study proposes an online dispatch rescheduling algorithm for WPPs participating in the LFC task to prevent over loading of transmission lines and hence cascading failures following severe frequency contingencies. The WPP dispatch limits are calculated based on line ampacity for each generation unit participating in the LFC task. Numerical simulations carried out in DigSilent PowerFactory on 39 bus IEEE standard test system, demonstrates the efficiency of suggested online dispatch rescheduling technique for keeping the loading rate of transmission lines, bus voltages and system frequency in range.

Recurrent Neural-Network-Based Maximum Frequency Deviation Prediction Using Probability Power Flow Dynamic Tool

This paper proposes a recurrent neural network (RNN)-based maximum frequency deviation forecasting model for power systems with high photovoltaic power (PV) penetration. The proposed RNN model extracts the nonlinear features and invariant structures exhibited in regional PV power output data and time-variable frequency data in case of contingency. To capture the regularity and random characteristics of PV power output, a probability power flow-dynamic tool (PPDT) for uncertain power system modeling has been developed. This tool considers all possible combinations of PV power generation patterns, even those with low probability, such as those caused by passing clouds. The results are verified by a comparison of various artificial intelligence methods using case studies from the South Korean power system. An online dispatch algorithm that considers the frequency constraints for a designated contingency can be implemented by using the proposed model.

Benchmarking online dispatch algorithms for Emergency Medical Services

Providers of Emergency Medical Services (EMS) face the online ambulance dispatch problem, in which they decide which ambulance to send to an incoming incident. Their objective is to minimize the fraction of arrivals later than a target time. Today, the gap between existing solutions and the optimum is unknown, and we provide a bound for this gap.Motivated by this, we propose a benchmark model (referred to as the offline model) to calculate the optimal dispatch decisions assuming that all incidents are known in advance. For this model, we introduce and implement three different methods to compute the optimal offline dispatch policy for problems with a finite number of incidents. The performance of the offline optimal solution serves as a bound for the performance of an – unknown – optimal online dispatching policy.We show that the competitive ratio (i.e., the worst case performance ratio between the optimal online and the optimal offline solution) of the dispatch problem is infinitely large; that is, even an optimal online dispatch algorithm can perform arbitrarily bad compared to the offline solution. Then, we performed benchmark experiments for a large ambulance provider in the Netherlands. The results show that for this realistic EMS system, when dispatching the closest idle vehicle to every incident, one obtains a fraction of late arrivals that is approximately 2.7 times that of the optimal offline policy. We also analyze another online dispatch heuristic, that manages to reduce this gap to approximately 1.9. This constitutes the first quantification of the gap between online and offline dispatch policies.

Economic and network feasible online power management for renewable energy integrated smart microgrid

Abstract In this paper, an online power management (OPM) problem is proposed using measured/short term forecasted data. It improves the cost and dynamic performance of microgrid along with the consideration of interval uncertainties in renewable energy and loads. Additional options of grid power trade and demand response (DR) are provided for operational cost reduction, consumer participation and enhanced network performance. A combination of stochastic weight tradeoff particle swarm optimization (SWT-PSO) and interval arithmetic (IA) is proposed to analyze the effects of interval uncertainties of nodal power injections (due to renewable energy sources (RES) and loads) on microgrid cost and power flow variables. The effectiveness of the proposed approach is investigated by adding different power balancing resources one by one, to the residential feeder of CIGRE LV benchmark microgrid. The results are found to be improved in terms of reduction in fuel and emission costs, improved nodal voltages and network feasible OPF solution in interval forms corresponding to system volatilities. Moreover, settling time of online dispatch is reduced, thereby improving the dynamic response of distributed energy resources (DERs). In addition, the paper justifies the use of RES, DR, grid power trade (over islanded mode) and SWT-PSO (over priority listing).

An Online Rolling Dispatch Optimization Strategy for the Access of Intermittent New Energy

For the volatility and intermittency of intermittent new energy like wind power, traditional dispatch model and technology are severely challenged. According to the characteristics that the prediction accuracy of wind power increases as time scale increases, this paper presents a multi-time scale active power dispatch model based on the traditional dispatch model, and proposes an active power dispatch hierarchical predicting control method on the base of model predictive control and multilevel hierarchical control during industrial control course. Finally, it gets the online rolling dispatch model and strategy for the access of intermittent energy.