Showing papers on "Microgrid published in 2016"

Review of Power Sharing Control Strategies for Islanding Operation of AC Microgrids

Abstract: Microgrid is a new concept for future energy distribution system that enables renewable energy integration. It generally consists of multiple distributed generators that are usually interfaced to the grid through power inverters. For the islanding operation of ac microgrids, two important tasks are to share the load demand among multiple parallel connected inverters proportionately, and maintain the voltage and frequency stabilities. This paper reviews and categorizes various approaches of power sharing control principles. Simultaneously, the control schemes are graphically illustrated. Moreover, various control approaches are compared in terms of their respective advantages and disadvantages. Finally, this paper presents the future trends.

Resilient Distribution System by Microgrids Formation After Natural Disasters

Abstract: Microgrids with distributed generation (DG) provide a resilient solution in the case of major faults in a distribution system due to natural disasters. This paper proposes a novel distribution system operational approach by forming multiple microgrids energized by DG from the radial distribution system in real-time operations to restore critical loads from the power outage. Specifically, a mixed-integer linear program is formulated to maximize the critical loads to be picked up while satisfying the self-adequacy and operation constraints for the microgrids formation problem by controlling the ON/OFF status of the remotely controlled switch devices and DG. A distributed multiagent coordination scheme is designed via local communications for the global information discovery as inputs of the optimization, which is suitable for autonomous communication requirements after the disastrous event. The formed microgrids can be further utilized for power quality control and can be connected to a larger microgrid before the restoration of the main grids is complete. Numerical results based on modified IEEE distribution test systems validate the effectiveness of our proposed scheme.

Big data driven smart energy management: From big data to big insights

Abstract: Large amounts of data are increasingly accumulated in the energy sector with the continuous application of sensors, wireless transmission, network communication, and cloud computing technologies. To fulfill the potential of energy big data and obtain insights to achieve smart energy management, we present a comprehensive study of big data driven smart energy management. We first discuss the sources and characteristics of energy big data. Also, a process model of big data driven smart energy management is proposed. Then taking smart grid as the research background, we provide a systematic review of big data analytics for smart energy management. It is discussed from four major aspects, namely power generation side management, microgrid and renewable energy management, asset management and collaborative operation, as well as demand side management (DSM). Afterwards, the industrial development of big data-driven smart energy management is analyzed and discussed. Finally, we point out the challenges of big data-driven smart energy management in IT infrastructure, data collection and governance, data integration and sharing, processing and analysis, security and privacy, and professionals.

Voltage Stability and Reactive Power Sharing in Inverter-Based Microgrids With Consensus-Based Distributed Voltage Control

Abstract: We propose a consensus-based distributed voltage control (DVC) that solves the problem of reactive power sharing in autonomous inverter-based microgrids with dominantly inductive power lines and arbitrary electrical topology. Opposed to other control strategies available thus far, the control presented here does guarantee a desired reactive power distribution in steady state while only requiring distributed communication among inverters, i.e., no central computing nor communication unit is needed. For inductive impedance loads and under the assumption of small phase angle differences between the output voltages of the inverters, we prove that the choice of the control parameters uniquely determines the corresponding equilibrium point of the closed-loop voltage and reactive power dynamics. In addition, for the case of uniform time constants of the power measurement filters, a necessary and sufficient condition for local exponential stability of that equilibrium point is given. The compatibility of the DVC with the usual frequency droop control for inverters is shown and the performance of the proposed DVC is compared with the usual voltage droop control via simulation of a microgrid based on the Conseil International des Grands Reseaux Electriques (CIGRE) benchmark medium voltage distribution network.

Bidding Strategy for Microgrid in Day-Ahead Market Based on Hybrid Stochastic/Robust Optimization

Abstract: This paper proposes an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG, and price responsive loads. The microgrid coordinates the energy consumption or production of its components, and trades electricity in both day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, and day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total cost of operation minus total benefit of demand. This formulation can be solved by mixed-integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a photovoltaic panel, a fuel cell, a micro-turbine, a diesel generator, a battery, and a responsive load show the advantage of stochastic optimization, as well as robust optimization.

Sizing and Analysis of Renewable Energy and Battery Systems in Residential Microgrids

Abstract: Accelerated development of eco-friendly technologies such as renewable energy, smart grids, and electric transportation will shape the future of electric power generation and supply. Accordingly, the power consumption characteristics of modern power systems are designed to be more flexible, which impact the system sizing. However, integrating these considerations into the design stage can be complex. Under these terms, this paper presents a novel model based on mixed integer linear programming for the optimization of a hybrid renewable energy system with a battery energy storage system in residential microgrids in which the demand response of available controllable appliances is coherently considered in the proposed optimization problem with reduced calculation burdens. The model takes into account the intrinsic stochastic behavior of renewable energy and the uncertainty involving electric load prediction, and thus proper stochastic models are considered. This paper investigates the effect of load flexibility on the component sizing of the system for a residential microgrid in Okinawa. Also under consideration are different operation scenarios emulating technical limitations and several uncertainty levels.

Microgrid supervisory controllers and energy management systems: A literature review

Abstract: Microgrids (MGs), featured by distributed energy resources, consumption and storage, are designed to significantly enhance the self-sustainability of future electric distribution grids. In order to adapt to this new and revolutionary paradigm, it is necessary to control MGs in intelligent and coordinated fashion. To this aim, a new generation of advanced Microgrid Supervisory Controllers (MGSC) and Energy Management Systems (EMS) has emerged. The aim of this paper is to summarize the control objectives and development methodologies in the recently proposed MGSC/EMS. At first, a classification of control objectives is made according to the definition of hierarchical control layers in MGs. Then, focusing on MGSC/EMS related studies, a detailed methodology review is given with emphasis on representative applications and research works. Finally, the conclusions are summarized and the proposals of future research directions in this area are given.

Droop-Based Distributed Cooperative Control for Microgrids With Time-Varying Delays

Abstract: This paper develops a droop-based distributed cooperative control scheme for microgrids under a switching communication network with non-uniform time-varying delays. We first design a pinning-based frequency/voltage controller containing a distributed voltage observer and then design a consensus-based active/reactive power controller, which are employed into the secondary control stage to generate the nominal set points used in the primary control stage for different distributed generators (DGs). By this approach, the frequencies and the weighted average value of all DGs’ voltages can be pinned to the desired values while maintaining the precise active and reactive power sharing. With the proposed scheme, each DG only needs to communicate with its neighbors intermittently, even if their communication networks are local and time-varying, and their variant delays may be non-uniform. Sufficient conditions on the requirements for the network connectivity and the delay upper bound that guarantee the stability and reliability of the microgrid are presented. The effectiveness of the proposed control scheme is verified by the simulation of a microgrid test system.

Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load

Abstract: A scenario-based robust energy management method accounting for the worst-case amount of renewable generation (RG) and load is developed in this paper. The economic and robust model is formulated to maximize the total exchange cost while getting the minimum social benefits cost at the same time. Uncertainty of RG and load is described as an uncertain set produced by interval prediction. Then, the Taguchi’s orthogonal array (OA) testing method is used to provide possible testing scenarios. A simple, but practical, search strategy based on OA is designed for solving the optimization problem. By optimizing the worst-case scenario, the energy management solution of the proposed model is robust against most of the possible realizations of the modeled uncertain set by Monte Carlo verification. Numerical cases on the typical microgrid system show the effectiveness of the model and solution strategy. In addition, the influence of exchange electricity price and other parameters are also discussed in the cases.

Next-Generation Shipboard DC Power System: Introduction Smart Grid and dc Microgrid Technologies into Maritime Electrical Netowrks

Abstract: In recent years, evidence has suggested that the global energy system is on the verge of a drastic revolution. The evolutionary development in power electronic technologies, the emergence of high-performance energy storage devices, and the ever-increasing penetration of renewable energy sources (RESs) are commonly recognized as the major driving forces of the revolution. The explosion in consumer electronics is also powering this change. In this context, dc power distribution technologies have made a comeback and keep gaining a commendable increase in research interest and industrial applications. In addition, the concept of flexible and smart distribution has also been proposed, which tends to exploit distributed generation and pack together the distributed RESs and local electrical loads as an independent and self-sustainable entity, namely a microgrid. At present, research in the area of dc microgrids has investigated and developed a series of advanced methods in control, management, and objective-oriented optimization that would establish the technical interface enabling future applications in multiple industrial areas, such as smart buildings, electric vehicles, aerospace/aircraft power systems, and maritime power systems.

A Combined Wavelet and Data-Mining Based Intelligent Protection Scheme for Microgrid

Abstract: This paper presents an intelligent protection scheme for microgrid using combined wavelet transform and decision tree. The process starts at retrieving current signals at the relaying point and preprocessing through wavelet transform to derive effective features such as change in energy, entropy, and standard deviation using wavelet coefficients. Once the features are extracted against faulted and unfaulted situations for each-phase, the data set is built to train the decision tree (DT), which is validated on the unseen data set for fault detection in the microgrid. Further, the fault classification task is carried out by including the wavelet based features derived from sequence components along with the features derived from the current signals. The new data set is used to build the DT for fault detection and classification. Both the DTs are extensively tested on a large data set of 3860 samples and the test results indicate that the proposed relaying scheme can effectively protect the microgrid against faulty situations, including wide variations in operating conditions.

Microgrid Scheduling With Uncertainty: The Quest for Resilience

Abstract: In recent years, natural disasters around the world have underscored the need for operative solutions that can improve the power grid resilience in response to low-probability high-impact incidents. The advent of microgrids (MGs) in modern power systems has introduced promising measures that can fulfil the power network resiliency requirements. This paper presents a two-stage stochastic programing approach to the optimal scheduling of a resilient MG. The impact of natural disasters on the optimal operation of MGs is modeled using a stochastic programming process. Other prevailing uncertainties associated with wind energy, electric vehicles, and real-time market prices are also taken into account. The proposed hourly scheme attempts to mitigate damaging impacts of electricity interruptions by effectively exploiting the MG capabilities. Incorporating AC network constraints in the proposed model offers a better solution to the security-constrained operation of MGs. The proposed model is linearized which offers robustness, simplicity, and computational efficiency in optimizing the MG operation. The effectiveness of proposed approach is illustrated using a large-scale MG test bed with a realistic set of data.

Power and Voltage Balance Control of a Novel Three-Phase Solid-State Transformer Using Multilevel Cascaded H-Bridge Inverters for Microgrid Applications

Abstract: This paper presents a new application of power and voltage balance control schemes for the cascaded H-bridge multilevel inverter (CHMI)-based solid-state transformer (SST) topology. To reduce load on the controller and simplify modulation algorithm, a master–slave control (MSC) strategy is designed for the dual active bridge (DAB) stage. The master controller executes all control and modulation calculations, and the slave controllers manage only device switching and protection. Due to the inherent power and dc-link voltage unbalance in cascaded H-bridge-based SST, this paper presents a compensation strategy based on three-phase dq decoupled current controller. An optimum zero-sequence component is injected in the modulation scheme so that the three-phase grid currents are balanced. Furthermore, to tightly regulate the output voltage of all the DAB modules to target value, a dynamic reference voltage method is also implemented. With this proposed control method, the three-phase grid currents and dc-link voltage in each module can be simultaneously balanced. Finally, simulation and experimental results are presented to validate the performance of the controller and its application to microgrid SST.

Microgrid stability: Classification and a review

Abstract: Microgrid is becoming an attractive concept to meet the increasing demands for energy and deal with air pollutions. Distributed energy sources (DERs) in Microgrid are usually interfaced with the utility grid by inverters, so the characteristics of Microgrid stability are much different from that of a traditional grid. However, the classifications, guidelines, and analysis method of Microgrid stability are well behind of the Microgrid development. In this paper, a Microgrid stability classification methodology is proposed on the basis of the of Microgrid characteristics investigation, which considers the Microgrid operation mode, types of disturbance and time frame. Then a comprehensive review of the body of research on Microgrid stability is presented in order to identify and advance the field. Finally, some challenges and suggestions of Microgrid stability for further researches are discussed.

Dynamic Energy Management System for a Smart Microgrid

Abstract: This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid’s system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

Droop-Free Distributed Control for AC Microgrids

Abstract: A cooperative distributed secondary/primary control paradigm for AC microgrids is proposed. This solution replaces the centralized secondary control and the primary-level droop mechanism of each inverter with three separate regulators: voltage, reactive power, and active power regulators. A sparse communication network is spanned across the microgrid to facilitate limited data exchange among inverter controllers. Each controller processes its local and neighbors' information to update its voltage magnitude and frequency (or, equivalently, phase angle) set points. A voltage estimator finds the average voltage across the microgrid, which is then compared to the rated voltage to produce the first-voltage correction term. The reactive power regulator at each inverter compares its normalized reactive power with those of its neighbors, and the difference is fed to a subsequent PI controller that generates the second-voltage correction term. The controller adds the voltage correction terms to the microgrid rated voltage (provided by the tertiary control) to generate the local voltage magnitude set point. The voltage regulators collectively adjust the average voltage of the microgrid at the rated voltage. The voltage regulators allow different set points for different bus voltages and, thus, account for the line impedance effects. Moreover, the reactive power regulators adjust the voltage to achieve proportional reactive load sharing. The third module, the active power regulator, compares the local normalized active power of each inverter with its neighbors' and uses the difference to update the frequency and, accordingly, the phase angle of that inverter. The global dynamic model of the microgrid, including distribution grid, regulator modules, and the communication network, is derived, and controller design guidelines are provided. Steady-state performance analysis shows that the proposed controller can accurately handle the global voltage regulation and proportional load sharing. An AC microgrid prototype is set up, where the controller performance, plug-and-play capability, and resiliency to the failure in the communication links are successfully verified.

Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning

Abstract: In this paper, we study a dynamic pricing and energy consumption scheduling problem in the microgrid where the service provider acts as a broker between the utility company and customers by purchasing electric energy from the utility company and selling it to the customers. For the service provider, even though dynamic pricing is an efficient tool to manage the microgrid, the implementation of dynamic pricing is highly challenging due to the lack of the customer-side information and the various types of uncertainties in the microgrid. Similarly, the customers also face challenges in scheduling their energy consumption due to the uncertainty of the retail electricity price. In order to overcome the challenges of implementing dynamic pricing and energy consumption scheduling, we develop reinforcement learning algorithms that allow each of the service provider and the customers to learn its strategy without a priori information about the microgrid. Through numerical results, we show that the proposed reinforcement learning-based dynamic pricing algorithm can effectively work without a priori information about the system dynamics and the proposed energy consumption scheduling algorithm further reduces the system cost thanks to the learning capability of each customer.

Energy Management for Joint Operation of CHP and PV Prosumers Inside a Grid-Connected Microgrid: A Game Theoretic Approach

Abstract: This paper mainly focuses on the energy management of microgrids (MGs) consisting of combined heat and power (CHP) and photovoltaic (PV) prosumers. A multiparty energy management framework is proposed for joint operation of CHP and PV prosumers with the internal price-based demand response. In particular, an optimization model based on Stackelberg game is designed, where the microgrid operator (MGO) acts as the leader and PV prosumers are the followers. The properties of the game are studied and it is proved that the game possesses a unique Stackelberg equilibrium. The heuristic algorithm based on differential evolution is proposed that can be adopted by the MGO, and nonlinear constrained programing can be adopted by each prosumer to reach the Stackelberg equilibrium. Finally, via a practical example, the effectiveness of the model is verified in terms of determining MGO's prices and optimizing net load characteristic, etc.

Distributed Tertiary Control of DC Microgrid Clusters

Abstract: A distributed control method is proposed to handle power sharing among a cluster of dc microgrids. The hierarchical control structure of microgrids includes primary, secondary, and tertiary levels. While the load sharing among the sources within a dc microgrid is managed through primary and secondary controllers, a tertiary control level is required to provide the higher level load sharing among microgrids within a cluster. Power transfer between microgrids enables maximum utilization of renewable sources and suppresses stress and aging of the components, which improves its reliability and availability, reduces the maintenance costs, and expands the overall lifespan of the network. The proposed control mechanism uses a cooperative approach to adjust voltage set points for individual microgrids and, accordingly, navigate the power flow among them. Loading mismatch among neighbor microgrids is used in an updating policy to adjust voltage set point and mitigate such mismatches. While the voltage adjustment policy handles the load sharing among the microgrids within each cluster, at a lower level, each microgrid carries a communication network that is in contact with the secondary control system. It is this lower level network that propagates voltage set points across all sources within a microgrid. Load sharing and set point propagation are analytically studied for the higher and lower level controllers, respectively. Experimental studies on two cluster setups demonstrate excellent controller performance and validate its resiliency against converter failures and communication losses.

Analysis and Optimization of Droop Controller for Microgrid System Based on Small-Signal Dynamic Model

Abstract: Droop control strategy enables the microgrid switch between grid-connected and islanded mode flexibly, and easily realizes the “plug and play” function of distributed generation and loads, which has recently aroused great concerns. However, small disturbances may occur during the changing process and eventually yield transient oscillation, thus the focus of microgrid control is how to switch smoothly within different operation modes. In order to improve the dynamic characteristics of an inverter-based microgrid, this paper derived a precise small-signal state-space model of the whole microgrid including droop controller, network, and loads. The key control parameters of the inverter and their optimum ranges, which greatly influence the damping frequency of oscillatory components in the transient response, can be obtained through eigenvalue analysis. In addition, genetic algorithm is introduced to search for optimal settings of the key parameters during time-domain simulation in MATLAB/Simulink. Simulation results verified the effectiveness of the proposed small-signal dynamic model and optimization algorithm, and enhanced the dynamic performance of the microgrid, which can be the reference for parameter design of droop control in low voltage microgrids.

Stochastic Optimal Operation of Microgrid Based on Chaotic Binary Particle Swarm Optimization

Abstract: Based on fuzzy mathematics theory, this paper proposes a fuzzy multi-objective optimization model with related constraints to minimize the total economic cost and network loss of microgrid. Uncontrollable microsources are considered as negative load, and stochastic net load scenarios are generated for taking the uncertainty of their output power and load into account. Cooperating with storage devices of the optimal capacity controllable microsources are treated as variables in the optimization process with the consideration of their start and stop strategy. Chaos optimization algorithm is introduced into binary particle swarm optimization (BPSO) to propose chaotic BPSO (CBPSO). Search capability of BPSO is improved via the chaotic search approach of chaos optimization algorithm. Tests of four benchmark functions show that the proposed CBPSO has better convergence performance than BPSO. Simulation results validate the correctness of the proposed model and the effectiveness of CBPSO.

A Two-Stage Model Predictive Control Strategy for Economic Diesel-PV-Battery Island Microgrid Operation in Rural Areas

Abstract: Microgrids consisting of diesel generators, storage devices, and renewable sources present an effective approach for an economic energy supply to rural areas. Advanced control methods are needed to improve the energy dispatch, enable a cost-efficient operation and guarantee an uninterrupted power supply. In particular, sudden variations in load demand or additional power supply from renewable sources are often unpredictable and underline the need for enhanced control. This paper presents an advanced control strategy for the optimal microgrid operation using a two-layer model predictive method. The first optimization layer presents an optimal control problem, based on real-time predictions of future power profiles, for the calculation of the optimal energy dispatch. To improve the robustness of the control strategy toward prediction errors, a boundary value problem is solved to adjust the diesel generator power in the second stage. The model predictive control framework is further used to adapt the weights of the forecast algorithm. Simulation studies are carried out by using real-world data to illustrate the performance and economic benefits of the proposed method. Results show the effectiveness of the control strategy in terms of computational feasibility, accuracy, increased robustness, and reduced cost.

A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids

Abstract: Microgrid equipped with heterogenous energy resources and a bank of energy storage devices presents the idea of small scale distributed energy management (DEM). DEM facilitates the minimization of the transmission and operation costs, peak load and environmental pollution. Microgrid also enables active customer participation by giving them the access to the real time information and control. The capability of fast restoration against physical/cyber attack, integration of renewable energy resources and information and communication technologies (ICT) make microgrid as an ideal candidate for distributed power systems. The energy management system of microgrid can perform real time energy forecasting of renewable resources, energy storage elements and controllable loads in making proper short term scheduling to minimize total operating costs. Cost benefit analysis of microgrid reveals that cooperation among different microgrids can play an important role in the reduction of import energy cost from the utility grid. Cooperation among microgrids in smart microgrid network (SMN) brings the energy sharing and management issues. In this paper we present a review of existing optimization objectives, constraints, solution approaches and tools used in microgrid energy management. This review paper can provide a foundation to embark on an in depth study in the area of energy management for smart microgrid network.