Ali Hooshmand

Power flow management of microgrid networks using model predictive control

In this paper, we present a power flow management method for a network of cooperating microgrids within the context of a smart grid by formulating the problem in a model predictive control framework. In order to reliably and economically provide the required power to the costumers, the proposed method enables the network of microgrids to share the power generated from their renewable energy sources and minimize the power needed from the micro gas turbines. To corroborate the viability of the proposed method, we will illustrate simulation results on a model consisting of three microgrids in a network.

Stochastic model predictive control method for microgrid management

This paper presents a stochastic model predictive control method for managing a microgrid. In order to reliably provide the required power for costumers, the proposed method enables the microgrid to use the renewable energy sources as much as possible while keeping the storage device to its maximum state of charge and minimizing the power generated by the micro gas turbine. The performance and effectiveness of the proposed method will be finally illustrated by simulating a microgrid model consisting of three nodes including a renewable generation source and a battery, customers, and a micro gas turbine.

A novel cost-aware multi-objective energy management method for microgrids

This paper proposes a multi-objective energy management method for microgrids which include local generation sources, grid connection, energy storage units and various loads. Minimization of the energy cost and maximization of batterys lifetime in a microgrid are considered as two main objectives which are optimized simultaneously. To achieve these objectives, microgrids central controller must find the best pattern for charging and discharging the battery. To this purpose, there is a need to have information about time-of-use (TOU) grid electricity rates, forecasted load profile and renewable generation levels. Model predictive control (MPC) policy is then utilized for solving the optimization problem and real-time implementation in a closed-loop framework. The performance and effectiveness of the proposed method is verified by simulating a microgrid model with real yearly data for the demand and renewable generation profiles and TOU rates. It is shown that the saving in energy cost can be increased considerably by applying the proposed MPC algorithm instead of a static energy management approach. Furthermore, the proposed algorithm is capable of regulating the battery usage based on the expected lifetime by considering the battery life span maximization objective.

Experimental Demonstration of a Tiered Power Management System for Economic Operation of Grid-Tied Microgrids

Power management of grid-tied microgrids including distributed generations (DGs) and storage devices can be challenging due to the intermittent and uncontrollable nature of many types of DGs, load variations in time, different grid power tariffs, availability of different options to balance the electric supply and demand, and other parameters. In order to operate a microgrid efficiently, the management system should accomplish two tasks. 1) It needs to be adaptive and optimize the microgrids performance by defining long-term (daily-based) directives or control strategies. 2) The management system should be able to operate and control the microgrid in real time and satisfy all operational constraints. To address the above-mentioned tasks, a comprehensive power management system that includes two control layers is developed in this paper. Concept for the proposed power management policy was demonstrated on an experimental microgrid system composed of lead-acid batteries, photovoltaic (PV) system, 3-kW peak load, and a utility connection.

Efficiency-driven control of dispatchable sources and storage units in hybrid energy systems

To operate as a stand-alone power system during grid power outages or peak price periods, local energy systems are equipped with storage devices and different distributed generations (DGs) such as diesel generators and renewable energy sources (RES). Power management of these hybrid systems will be challenging due to the intermittent and uncontrollable nature of many types of RES, load variations in time, availability of different energy source options with different cost function characteristics to balance the electric supply and demand, and other parameters. To operate the hybrid energy systems efficiently, a comprehensive power management method is proposed in this paper which includes two control layers. Proof of concept for the proposed power management policy was demonstrated on an experimental energy system composed of lead-acid batteries, diesel generator simulator, PV system, 3kW peak load and a utility connection.

A power management system for planned & unplanned grid electricity outages

To deal with frequent planned and unplanned grid power outages, local energy systems are becoming popular idea. These systems are equipped with storage devices and different distributed generations (DGs) such as diesel generators. Efficient and reliable operation of local power systems is complex since power outages are stochastic events, and also local energy suppliers have different operation costs, constraints, and efficiency characteristics. A power management system (PMS), consisting of two control layers is developed in this paper to address aforementioned complex operation. To validate the proposed method, energy system of a base transceiver station (BTS) in India is simulated. It composed of batteries, diesel generator, real BTS load data, and a utility connection. In addition, the real historical outage data from India has been utilized to simulate outage events.

A wavelet-based method for high resolution multi-step PV generation forecasting

Forecasts play a vital role in maintaining power system stability and maximizing economic benefits of distributed energy resources. The issue with PV generation forecasting is that it relies on forecasts of solar irradiation which, due to the complex, nonlinear relationship between humidity, pressure, temperature, and cloud transients, can be quite difficult to model. Two important decisions in the forecasting process are selection of the forecasted variable (model output) and selection of explanatory variables (model inputs). This paper proposes a new method to forecast PV generation using wavelet based input selection and an output variable that directly represents clouds transients. We model this cloud effect by first determining a clear sky model (CSM) and forecasting the difference between the CSM and actual measurements of global horizontal irradiance (GHI). Potential model inputs are first decomposed using wavelet multi resolution analysis and final input selection is based on the correlation between the inputs and output at various timescales. Two separate neural network structures are designed to separately forecast sunny and cloudy days. Using the high resolution forecast of GHI (20 min increments), the next days PV generation is determined. This method improves on the persistence method by 69% on sunny days, 26% on cloudy days.

BSS sizing and economic benefit analysis in grid-scale application

Grid-scale energy storage systems are attracting more attention because of increased public-awareness and declining prices. However, there is still one question which needs to be answered: when utilization of Battery Storage System (BSS) is economical? To address this question, problems of simultaneously sizing BSSs and optimal power sharing -with an objective of decreasing daily operational cost- is investigated to assess the economic viability of BSSs. The assessment is carried out by specializing the problem formulation to mid-sized C&I customers associated with PG&E and by simulating scenarios that differ in the size of load, PV installation, cost of BSS and participation in Demand Response (DR). Simulation results indicate that, using price projections from DOE and Navigant, BSSs can be used to shift loads economically (savings of 10%) around the year 2019. Furthermore, the effective daily savings, when participating in DR programs, is noted to be independent of the load, and that participating in DR does not require a significantly up-sized BSS.

A service-based approach toward management of grid-tied microgrids

This paper presents an energy management framework in which various services can be delivered by a microgrid to the utility. A service is defined in terms of an adjustment in power flow profile at the point of common coupling that should be enforced during the service period. A microgrid equipped with a diverse set of generations, storage units, and flexible demands can provide a range of services for reliable and economic operation of the grid. A multi-objective optimization approach is used to formulate the energy management problem based on service definition and operational cost of a microgrid. It is shown that a set of Pareto optimal solutions can be calculated for operation of a microgrid during each service period. Simulation results for two case studies related to peak shaving and minimum power fluctuation services are presented and discussed to verify the proposed approach.

A decentralized energy management system

The primary goal of an energy management system (EMS) in power networks is to balance the supply and demand in a cost efficient manner given its operating horizon, and uncertainties in generation due to renewable generators and in demand. This goal is formulated as the economic dispatch problem. A centralized energy management system faces issues in scalability due to introduction of new generator or storage units and in robustness due to failures in some of the entities in the grid including the EMS itself. To alleviate these complexities a versatile decentralized energy management system (d-EMS) is developed. The d-EMS embeds a decentralized solution to the economic dispatch problem (EDP) based on the alternating direction method of multipliers (ADMM) inside a decentralized implementation of the receding horizon control. The ADMM based algorithm solves the EDP for the scheduling horizon. The receding horizon control allows the system to adapt to changes in the forecasts and network configuration. Decentralized protocols to handle changes to the communication network of devices is provided. These device failure and addition protocols entail network information updates only, thanks to the simple initialization of the ADMM algorithm.