Dispatch Of Microgrid Research Articles

Adaptive Dynamic Programming and Its Application to Economic Dispatch in Microgrid: A Brief Overview

Both adaptive dynamic programming and other intelligent algorithms can solve the economic dispatch problem in the microgrid. Adaptive dynamic programming can reduce the computational burden, which the intelligent algorithms suffer from, by using function approximation structure to approximate performance index function. In recent years, it has been also widely used in economic dispatch in the microgrid. In this article, we introduce some recent research trends within the field of adaptive dynamic programming based economic dispatch. Adaptive dynamic programming is firstly reviewed. Then, the current research works about adaptive dynamic programming based economic dispatch are summarized and compared. Furthermore, we point out some topics for future studies.

Microgrid Operational Planning using a Hybrid Neural Network with Resource-aware Scenario Selection

Modern power systems continue to increase in complexity due to the intermittent behavior of distributed energy resources (DERs) and stochastic climate factors. Considering these factors simultaneously generates enumerable scenarios for decision-making within a microgrid (MG) dispatch policy. An MG dispatch policy can be formulated as a mixed integer linear program (MILP) and solved using traditional solution methods such as simplex, graphical method, etc. However, given the number of potential scenarios, these traditional solution methods incur a high computational burden to obtain near real-time solutions. Thus, dimension reduction techniques are needed to reduce the MILP's complexity. To address the need for a scalable solution method and reduced model complexity, we propose a 2-stage reconfigurable framework for near real-time MG operational planning. The proposed 2-stage reconfigurable framework is comprised of four modules: i) a resource-aware scenario selection (RSS) algorithm to identify the most probable scenarios, ii) an MG dispatch policy to obtain solutions or dispatch decisions for an MG system, iii) a neural network (NN) to map the most probable scenarios from RSS to their corresponding solutions from the MG dispatch policy and iv) a rule-based policy to monitor depreciating predictions from the NN. The RSS module is a probabilistic variate of a full factorial design (FFD) that assigns weights to test points defining the design space based on stochastic climate factors and the number of quartiles used to partition each factor's likelihood function. The most probable scenarios from RSS and their corresponding solutions from the proposed MG dispatch policy were both synthesized into a training set. NNs were then trained using this dataset to predict dispatch decisions for the IEEE 18-bus and the IEEE 33-bus. The proposed RSS reduced the state space by 77%, where the remaining states reflect 98% of the original state space. Furthermore, the NNs applied for MG operational planning showed robust predictive performance with loss function values of 0.0228 and 0.0563 for the IEEE 18-bus and the IEEE 33-bus, respectively.

Tri-stage optimal scheduling for an islanded microgrid based on a quantum adaptive sparrow search algorithm

An islanded microgrid includes a combined heating and power system as well as electric vehicles. It is a key method for increasing energy efficiency, reducing pollution, and achieving carbon neutrality. Considering the uncertainties caused by renewable energy, load, and electric vehicles, this study presents a modified tri-stage scheduling method to realise the real-time dispatch of the islanded microgrid. The novel tri-stage scheduling method incorporates an information feedback mechanism into the traditional tri-stage scheduling method to address the problem of the sub-optimal solution obtained by the traditional method, which results from the significant errors between the day-ahead forecast data and intraday forecast data. In addition, users are guided by the tri-stage real-time price to charge/discharge electric vehicles orderly to fill in the valley and shave the peak of the electric load. In this study, a quantum adaptive sparrow search algorithm is proposed based on the characteristics of the tri-stage scheduling optimisation model and the limitation that the original algorithm easily falls into a local optimum. In the case study, the superiority of the novel tri-stage scheduling method, modified algorithm, and orderly charging/discharging of electric vehicles proposed in this paper are evaluated in different seasons. In the vehicle-to-grid mode, the influence of different numbers of electric vehicles on the real-time dispatch of the islanded microgrid is analyzed and discussed.

Distributionally Robust Optimal Dispatching of CHP Microgrid considering Concentrating Solar Power and Uncertainty

In order to solve the problem of diversified low-carbon energy supply with renewable energy as the main body, concentrating solar power (CSP) stations are introduced to act as cogeneration units. Taking full advantage of the flexible coupling and multienergy complementarity of electric, heat, and gas, an economic dispatch method for combined heat and power microgrid systems (CHP microgrid) with interconnected electric, heat, and gas is proposed. First, build the CSP-CHP microgrid structure and model the main equipment. Then, aiming at the minimum operating cost of the system, a regular scheduling model of the CSP-CHP microgrid system is established. On this basis, in order to deal with the uncertainty of renewable energy output, a distributionally robust optimization (DRO) model is introduced. In the DRO model, the Kullback–Leibler (KL) divergence is used to construct an ambiguity set about the predicted error of renewable energy output, and finally, the CSP-CHP microgrid DRO economic dispatch model is established. Finally, the system is simulated and analyzed in a typical CSP-CHP microgrid system, and the feasibility and effectiveness of the proposed method are verified by analysis. In addition, the necessity of introducing CSP and the advantages of the DRO model is further explained by comparison.

Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm

Electricity can be provided to small-scale communities like commercial areas and villages through microgrid, one of the small-scale, advanced, and independent electricity systems out of the grid. Microgrid is an appropriate choice for specific purposes reducing emission and generation cost and increasing efficiency, reliability, and the utilization of renewable energy sources. The main objective of this paper is to elucidate the combined economic emission dispatch CEED problem in the microgrid to attain optimal generation cost. A combined cost optimization approach is examined to minimize operational cost and emission levels while satisfying the load demand of the microgrid. With this background, the authors proposed a novel improved mayfly algorithm incorporating Levy flight to resolve the combined economic emission dispatch problem encountered in microgrids. The islanded mode microgrid test system considered in this study comprises thermal power, solar-powered, and wind power generating units. The simulation results were considered for 24 hours with varying power demands. The minimization of total cost and emission is attained for four different scenarios. Optimization results obtained for all scenarios using IMA give a comparatively better reduction in system cost than MA and other optimization algorithms considered revealing the efficacy of IMA taken for comparison with the same data. The proposed IMA algorithm can solve the CEED problem in a grid-connected microgrid.

An adaptive-based control law for accurate frequency restoration and economic load dispatch in microgrid with clock drifts

This paper focuses on frequency regulation and the economic dispatch problems of microgrid primarily composed of multi energy resources. Due to different problems such as intermittency of renewable energy, unbalanced demand and supply, frequency fluctuation and so on in microgrid. These problems alternative stability and disturbance rejection present challenges to the design of controllers and management algorithms for voltage, frequency and power in all possible execution scenarios. In this work, a novel adaptive generalized distributed averaging integral controller has been designed. Goals of the designed controller are to realize accurate steady-state frequency recovery in the circumstances of clock drifts, cope with the uncertainty by dynamically updating of the control parameters while maintaining system stability, exchange generating data of each generator by the communication channel and, realize the optimal power distribution among the generators. Moreover, we add an additional controller to the hierarchical control scheme to distribute the load economically, the algorithm based on consistency ensures the optimal load distribution. Finally, the effectiveness of the designed control scheme is verified by simulation comparing it with other related distributed control methods.

An Economic Dispatch Method of Microgrid Based on Fully Distributed ADMM Considering Demand Response

Aiming at the problem that the existing alternating direction method of multipliers (ADMM) cannot realize totally distributed computation, a totally distributed improved ADMM algorithm that combines logarithmic barrier function and virtual agent is proposed. We also investigate economic dispatch for microgrids considering demand response based on day-ahead real-time pricing (RTP), which forms a source-load-storage collaborative optimization scheme. First, three general distributed energy sources (DERs), renewable energy resources (RESs), conventional DERs and energy storage systems (ESSs), are considered in the method. Second, the goal of economic dispatch is to minimize the sum of three energy generation costs and implement the optimal power allocation of dispatchable DERs. Specifically, the approach not only inherits the fast computational speed of ADMM but also uses barrier function and virtual agent to handle inequality and equality, respectively. Moreover, the approach requires no coordination center and only the communication between current agent and adjacent agent to achieve totally distributed solution for every iteration, which can preserve information privacy well. Finally, a 30-node microgrid system is used for case analysis, and the simulation results demonstrate the feasibility and effectiveness of the proposed approach. It can be found that, the proposed approach converges to the optima when p = 0.01, v = 100, t0 = 0.01 and μ = 2.

An optimal dispatch model of adiabatic compressed air energy storage system considering its temperature dynamic behavior for combined cooling, heating and power microgrid dispatch

Adiabatic compressed air energy storage (A-CAES) technology naturally has the ability of cogenerating cooling heating and electric power. It is a promising energy storage technology in the application of combined cooling, heating and power (CCHP) dispatch. This paper explores a new modelling method of the A-CAES for CCHP dispatch by taking the temperature dynamic behavior of the A-CAES into detailed consideration. With the help of the binary technique and piecewise approximation method, this A-CAES model is formulated based on mixed-integer linear programming (MILP), which can be easily solved. On the basis of this, an optimal day-ahead dispatch model for a CCHP microgrid containing an A-CAES is proposed. The empirical study based on a pilot plant and system data in China is carried out. The simulation results verify the effectiveness of the proposed A-CAES model. With the comparison of the results using the proposed A-CAES model and the conventional A-CAES model without considering temperature dynamic behavior, the results show that the system operation cost obtained by the proposed model can be 13.7% lower than the results obtained by the conventional A-CAES model.

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.

Optimal dispatch of microgrid based on reverse mutation sparrow search algorithm

Optimal dispatch of microgrid based on reverse mutation sparrow search algorithm

Optimal dispatching of microgrid based on improved moth-flame optimization algorithm based on sine mapping and Gaussian mutation

Because the traditional power generation method has caused certain damage to the environment, the microgrid system composed of renewable energy has been widely developed and applied. This paper studies distributed power sources including photovoltaics, wind turbines, energy storage systems, gas turbines, and fuel cells. Under the conditions of microgrid islands and grid-connected operation, the fuel cost, operation and maintenance cost, and the electricity transaction cost between the microgrid and the distribution network, establish the optimal objective function for the operating cost of the microgrid. At the same time, due to the standard moth-flame optimization algorithm having low optimization accuracy and are easy to fall into local optimal solution, an improved moth-flame optimization algorithm based on Sine mapping and Gaussian mutation is proposed. This algorithm is used to obtain the output of each distributed power source and total operating cost in a dispatch period. Finally, an example is used to verify the effectiveness and economy of the proposed model and the improved algorithm.

Multi-objective optimization dispatching of a micro-grid considering uncertainty in wind power forecasting

This paper presents a new multi-objective optimization dispatching method to optimize the output power of distributed generators of a micro-grid considering uncertainty in wind power forecasting with the aims of minimizing the operational cost and pollutant emission. To deal with uncertainty in wind power forecasting, an adaptive concept of confidence interval (ACCI) is proposed and probabilistic wind power interval forecasting model based on deterministic wind power forecasting and ACCI is developed to address this problem. Then, a comprehensive formulation with different operational constraints is applied to construct the multi-objective optimization dispatching model of a micro-grid considering uncertainty in wind power forecasting, and two-step solution methodology based on chaos and sinusoidal mapping multi-objective optimization bat algorithm (CSMOBA) and fuzzy theory set is employed to solve the multi-objective optimization dispatching problem. Eventually, simulation results demonstrate the proposed ACCI method can work well and obtain appropriate confidence level at each dispatching hour, and the proposed CSMOBA algorithm outperforms multi-objective optimization bat algorithm and well-known non-dominated sorting genetic algorithm II when applied in multi-objective optimization dispatching of micro-grid.

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.

Approximate Dynamic Programming Decompositions for Economic Dispatch in Microgrids

Three novel approximate dynamic programming algorithms based on the temporal, spatial, and spatiotemporal decomposition are proposed for the economic dispatch problem (EDP) in a distribution energy system with complex topology and many non-dispatchable renewable energy sources and energy storage systems (ESS). Computational efficiency of the proposed algorithms is compared and convergence to the optimal solution is shown in numeric experiments on the example of the two-day hourly EDP for the IEEE 33bw test network having 200+ consumers, 150+ energy storages, and 1000+ consuming devices.

Voltage/Frequency Regulation With Optimal Load Dispatch in Microgrids Using SMC Based Distributed Cooperative Control

Majority of the contemporary hierarchical control strategies for microgrids are either centralized or distributed, relying on leader-follower (or master-slave) consensus for secondary frequency and/or voltage regulation. Thus, in either case, these strategies are susceptible to single-point-failure (SPF). This potential research gap motivated the authors to propose a distributed three-layered hierarchical control strategy applicable to droop-controlled islanded AC microgrids. The proposed strategy can simultaneously ensure (i) frequency and voltage regulation of DGs within the prescribed frequency and voltage deviation limits as per IEC 60034-1 standard (i.e., ±2% and ±5%, respectively) without relying on leader-follower consensus at the secondary level, (ii) distributed economic dispatch of active power with minimum error, and (iii) distributed reactive power dispatch with plausible error. The proposed technique is fully distributed and shares the computation and communication burden among the neighboring nodes using a sparse communication network, thus, it is insusceptible to SPF. The feasibility of the proposed technique is guaranteed through various time-domain based numerical simulations executed in Matlab/Simulink under different loading conditions and microgrid expansion.

An Improved C-DEEPSO Algorithm for Optimal Active-Reactive Power Dispatch in Microgrids With Electric Vehicles

An Improved C-DEEPSO Algorithm for Optimal Active-Reactive Power Dispatch in Microgrids With Electric Vehicles

A Robust Economic Load Dispatch in Community Microgrid Considering Incentive-based Demand Response

Effective uncertainties management is required to realize stable operation for community microgrids consisting of electric loads, variable renewable energies (VREs), electric vehicles (EVs), etc. The authors propose a robust energy management (REM) controller for optimizing system operation against uncertainties. Practical solutions are provided to the microgrid system operator when power imbalances arise from unpredictable events. The functions of the proposed REM controller consist of (1) loads/VREs forecasting algorithm using artificial intelligence techniques, (2) day-ahead 24-hour unit commitment based on the prediction error, and (3) intra-day 1-hour economic load dispatch (ELD) algorithm that secures the feasible operation regions with controllable resources. This paper proposes a robust ELD method against the uncertainties which cause supply and demand mismatch. During unpredictable events, the mismatch is effectively treated and resolved using incentive-based demand response (IDR). The scheme of the IDR program based on genetic algorithm (GA) is offered by the system operator to the consumers in the community microgrid. The feasible solutions are provided to the consumers as an incentive to prevent the power imbalance problem.

A Distributed Hierarchical Control Framework for Economic Dispatch and Frequency Regulation of Autonomous AC Microgrids

Motivated by the single point of failure and other drawbacks of the conventional centralized hierarchical control strategy, in this paper, a fully distributed hierarchical control framework is formulated for autonomous AC microgrids. The proposed control strategy operates with a distinct three-layer structure, where: a conventional droop control is adopted at the primary layer; a distributed leaderless consensus-based control is adopted at the secondary layer for active power and, hence, frequency regulation of distributed generating units (DGUs); and the tertiary layer is also based on the distributed leaderless consensus-based control for the optimal power dispatch. Under the proposed strategy, the three constituent control layers work in a coordinated manner. Not only is the load dispatched economically with a negligible power mismatch, but also the frequencies of all the DGUs are regulated to the reference value. However, the frequency regulation is achieved without requiring any central leader agent that has been reported in the contemporary distributed control articles. As compared to the conventional centralized hierarchical control, the proposed strategy only needs local inter-agent interaction with a sparse communication network; thus, it is fully distributed. The formulated strategy is tested under load perturbations, on an autonomous AC microgrid testbed comprising both low-inertia-type (inverter-interfaced) and high-inertia (rotating)-type DGUs with heterogeneous dynamics, and found to successfully meet its targets. Furthermore, it can offer the plug-and-play operation for the DGUs. Theoretical analysis and substantial simulation results, performed in the MATLAB/Simulink environment, are provided to validate the feasibility of the proposed control framework.

Distributed cooperative control for economic dispatch and SOC balance in DC microgrids with vanadium redox batteries

Abstract With the high penetration of renewable-type resources, the distributed energy storage unit (ESU) will be a key component against the stochastic and intermittent characteristics of the renewable energy resource in the DC microgrid. The vanadium redox battery (VRB) is a promising ESU for the islanded microgrid applications. Therefore, this paper presents a consensus-based distributed cooperative control to alleviate voltage fluctuations, realize economic dispatch, and maintain the state of charge (SOC) balance simultaneously in the DC islanded microgrid with VRBs. Specifically, the proposed solution includes a consensus term that achieves convergence to a common incremental cost value and an additional term which used to eliminate power imbalance and ensure that the total generation is equal to the total consumption. Furthermore, the proposed method is subject to the sum injected/output power real-time equilibrium and the constraints of the SOC for simulating a more practical situation. Finally, the charging process and discharging process numerical simulations are employed in this paper to test the effectiveness of the presented algorithm in an islanded DC microgrid composed of six DERs.

Data-driven economic dispatch for islanded micro-grid considering uncertainty and demand response

The variability and intermittency of renewable energy and power load bring great pressure to the dispatch of Micro-Grid, especially intra-day dispatch. In order to reduce the intra-day dispatch pressure, this paper proposes a data-driven two-stage day-ahead dispatch model for islanded Micro-Grid. The first stage dispatch model considers multiple demand responses, and applies phase space reconstruction and machine learning to predict renewable energy output and power load. Multi-objective particle swarm optimization algorithm is used to solve the first stage dispatch model. For the obtained Pareto Front, weight multiple objectives by entropy weight method to get the optimal solution. Since the deviation between the predicted value and the actual value can lead to renewable energy curtailment or load loss, the role of the second stage is to predict the renewable energy curtailment and load loss after the first stage dispatch. Firstly, extreme gradient boosting is used to predict when renewable energy curtailment and load loss occur. Secondly, extreme learning machine is used to predict the amount of renewable energy curtailment and load loss at the corresponding time points. Finally, linear programming and mixed integer linear programming are used to solve the second stage dispatch model. By comparative cases analysis, simulation results show that the enhancement of demand response to system efficiency is at the cost of increasing dispatch cost and reducing reliability. In contrast, the proposed two-stage dispatch method using regulation reserve capacity not only reduces dispatch cost, but also improves system efficiency and reliability.