Nelson L. Diaz

Intelligent Distributed Generation and Storage Units for DC Microgrids—A New Concept on Cooperative Control Without Communications Beyond Droop Control

Low voltage dc microgrids have been widely used for supplying critical loads, such as data centers and remote communication stations. Consequently, it is important to ensure redundancy and enough energy capacity in order to support possible increments in load consumption. This is achieved by means of expansion of the energy storage system by adding extra distributed energy storage units. However, using distributed energy storage units adds more challenges in microgrids control, since stored energy should be balanced in order to avoid deep discharge or over-charge in one of the energy storage units. Typically, voltage droop loops are used for interconnecting several different units in parallel to a microgrid. This paper proposes a new decentralized strategy based on fuzzy logic that ensures stored energy balance for a low voltage dc microgrid with distributed battery energy storage systems by modifying the virtual resistances of the droop controllers in accordance with the state of charge of each energy storage unit. Additionally, the virtual resistance is adjusted in order to reduce the voltage deviation at the common dc bus. The units are self-controlled by using local variables only, hence, the microgrid can operate without relying on communication systems. Hardware in the loop results show the feasibility of the proposed method.

Voltage scheduling droop control for State-of-Charge balance of distributed energy storage in DC microgrids

Due to higher power quality, lower conversion loss, and more DC loads, there has been an increasing awareness on DC microgrid. Previous emphasis has been on equal power sharing among different units in the DC microgrid, while overlooking the coordination of the energy storage units to maintain the State-of-Charge balance. In this paper, a new droop method based on voltage scheduling for State-of-Charge balance is proposed to keep the SoC balance for the energy storage units. The proposed method has the advantage of avoiding the stability problem existed in traditional methods based on droop gain scheduling. Simulation experiment is taken in Matlab on a DC microgrid with two distributed energy storage units. The simulation results show that the proposed method has successfully achieved SoC balance during the load changes while maintaining the DC bus voltage within the allowable range.

Mixed-Integer-Linear-Programming-Based Energy Management System for Hybrid PV-Wind-Battery Microgrids: Modeling, Design, and Experimental Verification

Microgrids are energy systems that aggregate distributed energy resources, loads, and power electronics devices in a stable and balanced way. They rely on energy management systems to schedule optimally the distributed energy resources. Conventionally, many scheduling problems have been solved by using complex algorithms that, even so, do not consider the operation of the distributed energy resources. This paper presents the modeling and design of a modular energy management system and its integration to a grid-connected battery-based microgrid. The scheduling model is a power generation-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data. The operation of the microgrid is complemented with a supervisory control stage that compensates any mismatch between the offline scheduling process and the real time microgrid operation. The proposal has been tested experimentally in a hybrid microgrid at the Microgrid Research Laboratory, Aalborg University.

Fuzzy-logic-based gain-scheduling control for state-of-charge balance of distributed energy storage systems for DC microgrids

A microgrid is an integration of distributed energy sources, loads and energy storage systems. Indeed, energy storage systems are required in order to ensure reliability and power quality because of the intermittent nature of renewable energy sources and changes of load demand. Apart from that, the use of distributed energy storage units provides redundancy to the system and support possible increments in load consumption. In consequence, the control strategy used in the microgrid must take into account the stored energy balance between distributed energy storage units in order to avoid over-charge or deep-discharge in one of the energy storage units. Primary control in a microgrid is responsible for power sharing among units; and droop control is typically used in this stage. This paper proposes a modular and decentralized gain-scheduling control strategy based on fuzzy logic that ensures balanced stored energy among distributed energy storage units, as well as low voltage deviation in a DC microgrid. Hardware in the loop simulations show the performance of the proposed control strategy.

Implementation of Nonlinear power flow controllers to control a VSC

The voltage source converters VSCpsilas are power converters highly used in systems of distributed generation, HVDCpsilas and motor drivers, thanks to the feasibility and facility to control the active and reactive power independently, in a fast and effective way. This paper presents the analysis, design and results obtained in digital simulation and in physical implementation, of nonlinear controllers applied to a VSC prototype, to control independently the active and reactive power flow. There are two control strategies. Firstly a fuzzy control based on linguistic rules was proposed, however it present a high computational cost in time, hence was proposed an Artificial Neural Network as a dynamic emulator of the fuzzy control. The computational time in simulation was compared between the two intelligent controllers. All the simulations were done in (MATLABreg/SIMULINKreg). Finally the system composed by the VSC and the controllers was implemented.

Fuzzy droop control loops adjustment for stored energy balance in distributed energy storage system

The study of isolated AC microgrid has been under high interest due to the integration of renewable energy resources especially for remote areas, or to improve the local energy reliability. The current trend is oriented to distributed renewable energy sources and their corresponding energy storage system, in order to smooth the variations at the prime energy generator. In this paper, a decentralized strategy based on fuzzy logic is proposed in order to balance the state of charge of distributed energy storage systems in low-voltage three phase AC microgrid. The proposed method weights the action of conventional droop control loops for battery based distributed energy storage systems, in order to equalize their stored energy. The units are self-controlled by using local variables, hence, the microgrid can operate without communication systems. Frequency and voltage bus signaling are used in order to coordinate the operation of the microgrid under different stages for charging batteries. Simulation results show the feasibility of the proposed method.

Economic power dispatch of distributed generators in a grid-connected microgrid

Grid-connected microgrids with storage systems are reliable configurations for critical loads which can not tolerate interruptions of energy supply. In such cases, some of the energy resources should be scheduled in order to coordinate optimally the power generation according to a defined objective function. This paper defines a generationside power scheduling and economic dispatch of a grid-connected microgrid that supplies a fixed load and then, the scheduling is enhanced by including penalties in order to increase the use of the renewable energy sources and guarantee a high state of charge in the storage system for the next day. Linear models are proposed for the scheduling which are implemented in GAMS. The microgrid model is obtained deploying MATLAB/Simulink toolbox and then downloaded into dSPACE 1006 platform based on real-time simulation to test the economic dispatch. A compromise between cost and use of renewable energy is achieved.

Generation-side power scheduling in a grid-connected DC microgrid

In this paper, a constrained mixed-integer programming model for scheduling the active power supplied by the generation units in storage-based DC microgrids is presented. The optimization problem minimizes operating costs taking into account a two-stage mode operation of the energy storage system so that a more accurate model for optimization of the microgrid operation can be obtained. The model is used in a particular grid-connected DC microgrid that includes two renewable energy sources and an energy storage system which supply a critical load. The results of the scheduling process are including in simulation by establishing a MATLAB/Simulink model of the microgrid and setting several initial conditions of the state of charge of the energy storage system. As a result, we obtain reductions in costs and at the same time guarantee safe levels of state of charge to increase the life-time of the energy storage system.

Fuzzy MPP method improved by a short circuit current estimator, applied to a grid-connected PV system

This paper presents an intelligent fuzzy method for maximum power point tracking (MPPT) of a single grid-connected photovoltaic system. The fuzzy inference system can easily synthesize the algorithm to perform MPPT. The MPPT fuzzy method is improved by weighting the control action by the short-circuit current. Hence, it is proposed an estimator of the short-circuit current based on a Takagi-Sugeno fuzzy model of the photovoltaic generator. The short-circuit current can be estimated under any weather condition in real time, without disconnecting the photovoltaic generator and measuring it. Simulation results show attractive features such as, accuracy, fast response, good dynamic performance and efficiency in the conversion process.

Optimal power scheduling for a grid-connected hybrid PV-wind-battery microgrid system

In this paper, a lineal mathematical model is proposed to schedule optimally the power references of the distributed energy resources in a grid-connected hybrid PV-wind-battery microgrid. The optimization of the short term scheduling problem is addressed through a mixed-integer linear programming mathematical model, wherein the cost of energy purchased from the main grid is minimized and profits for selling energy generated by photovoltaic arrays are maximized by considering both physical constraints and requirements for a feasible deployment in the real system. The optimization model is tested by using a real-time simulation of the model and uploaded it in a digital control platform. The results show the economic benefit of the proposed optimal scheduling approach in two different scenarios.