News

Filter by type:

Sort by year:
Journal Paper Conference Paper Book Chapter Book Patent

Optimal coordinated bidding of a profit-maximizing EV aggregator under uncertainty

Yelena Vardanyan, Frederik Banis, S. A. Pourmousavi, and Henrik Madsen
Conference Paper Submitted for review to the ENERGYCON Conference, Limassol, Cyprus, June 3-7, 2018

Abstract

An aggregator acts as a middleman between the small customers and the system operator (SO) offering a mutually beneficial agreement to trade electric power, where each market player (system operator, aggregator and electric vehicle (EV owner) has its own economic incentives. The EV aggregator aims to maximize its profit while trading energy and providing balancing power in wholesale markets. This paper develops a stochastic and dynamic mixed integer linear program (SDMILP) for optimal coordinated bidding of an EV aggregator to maximize its profit from participating in competitive dayahead and real-time markets. Under uncertain day-ahead and real-time market prices as well as fleet mobility, the proposed SD-MILP model finds optimal EV charging/discharging plans for every EV. The degradation costs of EV batteries are modeled. To reflect the continuous clearing nature of the real-time market, rolling planning is applied which allows re-forecasting and redispatching. The proposed SD-MILP is used to derive a bidding curve of an aggregator managing 1000 EVs.

The Impact of Temperature On Battery Degradation for Large-Scale BESS in PV Plant

Md Mehedi Hasan, S. A. Pourmousavi, Feifei Bai, and Tapan Kumar Saha
Conference Paper Accepted for oral presentation in the AUPEC, Melbourne, Australia, November 19-22, 2017

Abstract

Excessive high temperature is an important factor for battery power and capacity degradation. Every charge-discharge activity escalates cell temperature, which results in higher degradation rates. Therefore, considering the impact of charge-discharge activities on battery temperature and consequently degradation rate is indispensable step before establishing an optimal operation strategy for batteries. This paper describes a comprehensive investigation on the effect of battery charge-discharge on temperature and degradation. One year of operational data from a utility-scale solar photovoltaic (PV) plant with battery storage facility is used for this investigation. A strong correlation between battery temperature and discharge activities is identified, which can result in an excessive degradation of the battery.

An advance retail electricity market for active distribution systems and home microgrid interoperability based on game theory

Mousa Marzband, Masoumeh Javadi, S. A. Pourmousavi, and Gordon Lightbody
Journal Paper submitted to the Electric Power Systems Research, 2017

Abstract

In the future active distribution systems, market mechanisms are necessary to fully exploit the potential of the active customers, known as Prosumers. This paper offers an advanced retail electricity market based on game theory for optimal operation of home microgrids (H-MGs) and their interoperability. The proposed market accommodates any number of retailers and prosumers incorporating different generation sources, storage devices, retailers, and demand response resources. The proposed market is formulated with three different players, namely generating, consuming, and retailer players. The optimal solution is achieved by application of the Nikaido-Isoda Relaxation Algorithm (NIRA) in a gaming structure. The uncertainty of the players are modelled using suitable statistical models. A comprehensive simulation study is carried out to reveal the effectiveness of the proposed method in lowering the market clearing price (MCP), H-MG interoperability, and the environment of local generation consumption.

A framework for controlling electricity load in integrated energy systems

G. De Zotti, S. A. Pourmousavi, H. Madsen, and N. K. Poulsen
Conference Paper Submitted to the Power-Gen Europe Conference, Cologne, Germany, June 27-29, 2017

Abstract

The future power system will experience a large amount of renewable generation with highly stochastic and partly unpredictable characteristics. To overcome this issue, new exibility resources are needed to participate in the power system operation. Some of the new flexibility resources are linked to the electricity system, but they are managed outside of the electrical network by other energy sectors. To this end, an integrated energy system is needed to exploit such cross-sectoral opportunities. This further requires unique operational strategies for power systems in the new era. In this paper, the mainstream approaches for the energy system operation are briefly introduced. Afterwards, the Control-Based Approach for the distribution-level energy management is described in more details. Concerns related to the CB approach are investigated and the opportunities risen from such approach are listed in relation to the future integrated energy markets. Finally, multiple solutions are hypothesised to mitigate one of the most fundamental concern about the CB proposal.

EXTENDED ABSTRACT: A Control-Based Approach for Solving Ancillary Service Problems in Smart Grids

G. De Zotti, S. A. Pourmousavi, H. Madsen, and N. K. Poulsen
Conference Paper Accepted for oral presentation at COST TD1207 Conference, Modena, Italy, March 29-31, 2017

Abstract

The future power system will experience a large amount of RES generation with highly stochastic and unpredictable characteristics. Providing ancillary services is expected to become more severe to handle by increasing the level of stochasticity. In the new paradigm, new Flexibility Resources (FR) are needed to participate in the power systems operation by altering their generation and/or consumption when needed. This can be driven by a direct control or a real-time electricity price. Despite the existing market mechanism at the high level of the grid which represents actual grid condition in real-time, consumers at the lower level receive fixed rate or time-of-use (ToU) retail prices determined by the utility. Such pricing mechanism does not exploit the smaller FRs potential. As a result, it is important to develop a new structure for the retail electricity price. In order to exploit the exibility potential at the prosumers side and satisfy the operational needs at higher levels, we suggest a real-time retail pricing mechanism where every system operator contributes with an additional price component. Then, all additional prices are combined and broadcasted to the prosumers. At the operators' level, a Control-Based Approach (CBA) is used in the optimization problem, as it can handle stochasticity, non-linearity and dynamics. CBA can also support in the Integrated Energy System operation. By adopting a price-based control, the strategy also becomes cheap and scalable.

Towards a Smart Energy Operating System: A Control-Based Approach

G. De Zotti, S. A. Pourmousavi, H. Madsen, and N. K. Poulsen
Journal Paper Submitted for possible publication in Energy special issue named after the 2nd International Conference of the Skoltech Center for Energy Systems: “Shaping research in integrated gas-, heat- and electric- energy infrastructures”, 2017

Abstract

Future power system will experience a large amount of renewable generation with highly stochastic and partlyunpredictable characteristics. To overcome this issue, new flexibility resources are needed to participate inthe power system operation. Some of the new flexibility resources are linked to the electricity system, butthey are managed outside of the electrical network by other energy sectors. To this end, an integratedenergy system (IES) is needed to exploit such cross-sectoral opportunities. This further requires uniqueoperational strategies for power systems in the new era. In this paper, two mainstream approaches for thefuture energy system operation are briefly introduced. Then, potentials of the control-based (CB) approachfor the distribution-level energy management are described in details. Concerns related to the CB approachare investigated and the opportunities risen from such approach are listed in relation to the future integratedenergy markets. Finally, multiple solutions are hypothesized to mitigate one of the most fundamental concernabout the CB proposal.

A Two-Layer Incentive-Based Controller for Aggregating BTM Energy Storage Devices

S. A. Pourmousavi, M. Parandehgheibi, Kiyoshi Nakayama, and Ratnesh K. Sharma
Patent U.S. Patent: Under preparation by the legal team, 2016

Abstract

The total energy bill for commercial/industrial (C/I) loads consists of two parts: Energy charge which is proportional to the total energy consumption, and demand charge which is proportional to the peak power consumption. The demand charge is known to be more than 50% of the total cost. To reduce demand charge, C/I customers are equipped with behind-the-meter (BTM) battery storage. However, battery technologies are still expensive and the battery in such applications sits idle for most of the day. In order to create extra revenue for the C/I customer, which further increase the economic benefit of BTM devices, we proposed a two–layer incentive –based controller to aggregate small BTM devices for participation in the wholesale energy market. In the aggregator level, incentive signals will be generated and communicated to each customer based on predicted values of wholesale energy market prices. At the local level, a local controller is designed to optimally create battery operation profile for the next day based on the Time-of-Use (ToU) and demand charge prices as well as incentive signal received from the aggregator. Eventually, the local controller commit to a certain level of generation to consume/provide in each hour of the day ahead. Simulation results show an average of 12% extra revenue for the C/I customers considering battery degradation cost, and uncertainties related to predicted values.

A Novel Optimal Battery Sizing Algorithm for Behind-the-Meter Application considering Participation in Demand Response Programs and Demand Charge Reduction

Ali Hooshmand, S. A. Pourmousavi, Ratnesh K. Sharma, Shankar Mohan
Patent U.S. Patent: Under preparation by the legal team, 2016

Abstract

Unfortunately, the content of the patent cannot be revealed to the public before filing with the U.S. Patent Office.

Resilient Battery Charging Strategies to Reduce Battery Degradation and Self-Discharging

S. A. Pourmousavi, Babak Asghari, Ratnesh K. Sharma
Patent U.S. Patent: Under preparation by the legal team, 2016

Abstract

Unfortunately, the content of the patent cannot be revealed to the public before filing with the U.S. Patent Office.

An Innovative Framework to Combine Cyclic and Calendar Aging Models

S. A. Pourmousavi, Babak Asghari, Ratnesh K. Sharma
Patent U.S. Patent: Under preparation by the legal team, 2016

Abstract

Unfortunately, the content of the patent cannot be revealed to the public before filing with the U.S. Patent Office.

Data-Driven Battery Aging Model using Statistical Analysis and Artificial Intelligence

S. A. Pourmousavi, Babak Asghari, Ratnesh K. Sharma
Patent U.S. Patent Application 20160239592, filed on March 2016

Abstract

Battery aging is a complex phenomenon involving too many operational parameters. An accurate and fast battery aging model can improve performance of battery sizing models and management systems significantly. This way, many researchers proposed different models to estimate battery capacity degradation (i.e., aging). Most of these studies simplify the problem by only including 1 to 3 parameters in their proposed model. Additionally, no evidence is given to support the hypotheses behind selecting some parameters and ignoring other ones. Furthermore, some of the proposed models are built upon very complicated chemical reactions of the battery which are computationally expensive and require so many chemical parameters of the battery to be known. In this study, we have developed a neural network-based battery aging model (particularly Li-Ion battery) whose parameters are chosen based on comprehensive analytical (statistical) study. The interactions among different parameters and their higher order behavior has also been captured in the analytical study and are hypothesized in the final model using statistical learning techniques. The proposed model include ambient temperature, maximum and minimum state of charge (SOC) of the battery, charging and discharging rates, and energy throughput. Overall mean squared error of 98% has been achieved with the developed neural network model. The trained neural network can be easily and effectively port to any other studies. It is fast and has minimal computational efforts. Besides the neural network model, analytical approaches (statistical learning in this study) are utilized to develop other type of battery aging model with multiple regression and least square method. Overall, the neural network model outperforms other proposed models.

Method for Real-Time Power Management of a Grid-Tied Microgrid to Extend Storage Lifetime and Reduce Cost of Energy

Babak Asghari, Ratnesh K. Sharma, S. A. Pourmousavi
Patent U.S. Patent 9,020,649 - 2015

Abstract

A management framework is disclosed that achieves maximum energy storage device lifetime based on energy storage device life estimation and the price of energy

Electrical Circuits I&II: Solution Manual

S. A. Pourmousavi
Book 320 pages, Payam Daneshgahi press 2009 | Isfahan, Iran | ISBN:978-864-8622-43-0. (In Farsi)
first page of my book

Description

This book is a comprehensive solution manual and briefing courses for Electrical Circuits of first and second-order. Every chapter starts with a short course and notes about a specific topic in Electrical Circuits which is followed by solving numerous questions. Every question is explained in details and the equations are dervied step-by-step. This is a valuable source to learn this difficult toipc in Electrical Engineering in depth.

Assessing the potential of plug-in electric vehicles in active distribution networks

Reza Ahmadi Kordkheili, S. A. Pourmousavi, Mehdi Savaghebi, Josep M Guerrero, and Mohammad Hashem Nehrir
Journal Paper Energies, 9(1):34, 2016

Abstract

A multi-objective optimization algorithm is proposed in this paper to increase the penetration level of renewable energy sources (RESs) in distribution networks by intelligent management of plug-in electric vehicle (PEV) storage. The proposed algorithm is defined to manage the reverse power flow (PF) from the distribution network to the upstream electrical system. Furthermore, a charging algorithm is proposed within the proposed optimization in order to assure PEV owner’s quality of service (QoS). The method uses genetic algorithm (GA) to increase photovoltaic (PV) penetration without jeopardizing PEV owners’ (QoS) and grid operating limits, such as voltage level of the grid buses. The method is applied to a part of the Danish low voltage (LV) grid to evaluate its effectiveness and capabilities. Different scenarios have been defined and tested using the proposed method. Simulation results demonstrate the capability of the algorithm in increasing solar power penetration in the grid up to 50%, depending on the PEV penetration level and the freedom of the system operator in managing the available PEV storage.

Multi-timescale power management for islanded microgrids including storage and demand response

S. A. Pourmousavi, M. Hashem Nehrir, and Ratnesh K. Sharma
Journal Paper IEEE Transactions on Smart Grid, 6(3):1185–1195, 2015

Abstract

Power management is an essential tool for microgrid (MG) safe and economic operation, particularly in the islanded operation mode. In this paper, a multi-timescale costeffective power management algorithm (PMA) is proposed for islanded MG operation targeting generation, storage, and demand management. Comprehensive modeling, cost, and emission calculations of the MG components are developed in this paper to facilitate high accuracy management. While the MGs overall power management and operation is carried out every several minutes to hours, depending on the availability of the required data, simulation for highly dynamic devices, such as batteries and electric water heaters (EWHs) used for demand response (DR), are performed every minute. This structure allows accurate, scalable, and practical power management taking into consideration the intrainterval dynamics of battery and EWHs. Two different on/off strategies for EWH control are also proposed for DR application. Then, the PMA is implemented using the two different DR strategies and the results are compared with the no-DR case. Actual solar irradiation, ambient temperature, nonEWH load demand, and hot water consumption data are employed in the simulation studies. The simulation results for the MG studied show the effectiveness of the proposed algorithm to reduce both MGs cost and emission.

Real-time demand response through aggregate electric water heaters for load shifting and balancing wind generation

S. A. Pourmousavi, Stasha N. Patrick, and M. Hashem Nehrir
Journal Paper IEEE Transactions on Smart Grid, 5(2):769–778, 2014

Abstract

Demand response (DR) has shown to be a promising tool for balancing generation and demand in the future power grid, specifically with high penetration of variable renewable generation, such as wind. This paper evaluates thermostat setpoint control of aggregate electric water heaters (EWHs) for load shifting, and providing desired balancing reserve for the utility. It also assesses the economic benefits of DR for the customers through time-of-use pricing. Simulation results reveal the achievement of the economic benefits to the customers while maintaining their comfort level and providing a large percentage of desired balancing reserve at the presence of wind generation.

Introducing dynamic demand response in the LFC model

S. A. Pourmousavi and M. Hashem Nehrir
Journal Paper IEEE Transactions on Power Systems, 29(4):1562–1572, 2014

Abstract

Demand response (DR) has proved to be an inevitable part of the future grid. Much research works have been reported in the literature on the benefits and implementation of DR. However, little works have been reported on the impacts of DR on dynamic performance of power systems, specifically on the load frequency control (LFC) problem. This paper makes an attempt to fill this gap by introducing a DR control loop in the traditional LFC model (called LFC-DR) for a single-area power system. The model has the feature of optimal operation through optimal power sharing between DR and supplementary control. The effect of DR communication delay in the controller design is also considered. It is shown that the addition of the DR control loop increases the stability margin of the system and DR effectively improves the system dynamic performance. Simulation studies are carried out for single-area power systems to verify the effectiveness of the proposed method.

Real-time central demand response for primary frequency regulation in microgrids

S. A. Pourmousavi and M. Hashem Nehrir
Journal Paper IEEE Transactions on Smart Grid, 3(4):1988–1996, 2012

Abstract

Providing ancillary services for future smart microgrid can be a challenging task because of lack of conventional automatic generation control (AGC) and spinning reserves, and expensive storage devices. In addition, strong motivation to increase the penetration of renewable energy in power systems, particularly at the distribution level, introduces new challenges for frequency and voltage regulation. Thus, increased attention has been focused on demand response (DR), especially in the smart grid environment, where two-way communication and customer participation are part of. This paper presents a comprehensive central DR algorithm for frequency regulation, while minimizing the amount of manipulated load, in a smart microgrid. Simulation studies have been carried out on an IEEE 13-bus standard distribution system operating as a microgrid with and without variable wind generation. Simulation results show that the proposed comprehensive DR control strategy provides frequency (and consequently voltage) regulation as well as minimizing the amount of manipulated responsive loads in the absence/presence of wind power generation.

Very short-term wind speed prediction: a new artificial neural network–markov chain model

S. A. Pourmousavi and M. M. Ardehali
Journal Paper Energy Conversion and Management, 52(1):738–745, 2011

Abstract

As the objective of this study, artificial neural network (ANN) and Markov chain (MC) are used to develop a new ANN–MC model for forecasting wind speed in very short-term time scale. For prediction of very short-term wind speed in a few seconds in the future, data patterns for short-term (about an hour) and very short-term (about minutes or seconds) recorded prior to current time are considered. In this study, the short-term patterns in wind speed data are captured by ANN and the long-term patterns are considered utilizing MC approach and four neighborhood indices. The results are validated and the effectiveness of the new ANN–MC model is demonstrated. It is found that the prediction errors can be decreased, while the uncertainty of the predictions and calculation time are reduced.

An innovative hybrid algorithm for very short-term wind speed prediction using linear prediction and markov chain approach

S. A. Pourmousavi, G. H. Riahy, and D. Mazhari
Journal Paper International Journal of Green Energy, 8(2):147–162, 2011

Abstract

A new hybrid algorithm using linear prediction and Markov chain is proposed in order to facilitate very short-term wind speed prediction. First, the Markov chain transition probability matrix is calculated. Then, linear prediction method is applied to predict very short-term values. Finally, the results are modified according to the long-term pattern by a nonlinear filter. The results from proposed method are compared by linear prediction method, persistent method and actual values. It is shown that the prediction-modification processes improves very short-term predictions, by reducing the maximum percentage error and mean absolute percentage error, while it retains simplicity and low CPU time and improvement in uncertainty of prediction.

Real-time energy management of a stand-alone hybrid wind-microturbine energy system using particle swarm optimization

S. A. Pourmousavi, M. Hashem Nehrir, Christopher M. Colson, and Caisheng Wang
Journal Paper IEEE Transactions on Sustainable Energy, 1(3):193–201, 2010

Abstract

Energy sustainability of hybrid energy systems is essentially a multiobjective, multiconstraint problem, where the energy system requires the capability to make rapid and robust decisions regarding the dispatch of electrical power produced by generation assets. This process of control for energy system components is known as energy management. In this paper, the application of particle swarm optimization (PSO), which is a biologically inspired direct search method, to find real-time optimal energy management solutions for a stand-alone hybrid wind-microturbine (MT) energy system, is presented. Results demonstrate that the proposed PSO-based energy management algorithm can solve an extensive solution space while incorporating many objectives such as: minimizing the cost of generated electricity, maximizing MT operational efficiency, and reducing environmental emissions. Actual wind and end-use load data were used for simulati on studies and the well-established sequential quadratic programming optimization technique was used to validate the results obtained from PSO. Promising simulation results indicate the suitability of PSO for real-time energy management of hybrid energy systems.

Ownership cost calculations for distributed energy resources using uncertainty and risk analysis

S. A. Pourmousavi, Mahdi Behrang-Rad, Ali Jahanbani Ardakani, and M. Hashem Nehrir
Conference Paper Publicaly available on ArXiv, September 2017

Abstract

Ownership cost calculation plays an important role in optimal operation of distributed energy resources (DERs) and microgrids (MGs) in the future power system, known as smart grid. In this paper, a general framework for ownership cost calculation is proposed using uncertainty and risk analyses. Four ownership cost calculation approaches are introduced and compared based on their associated risk values. Finally, the best method is chosen based on a series of simulation results, performed for a typical diesel generator (DiG). Although simulation results are given for a DiG (as commonly used in MGs), the proposed approaches can be applied to other MG components, such as batteries, with slight modifications, as presented in this paper. The analyses and proposed approaches can be useful in MG optimal design, optimal power flow, and market-based operation of the smart grid for accurate operational cost calculations.

A Two-Layer Incentive-Based Controller for Aggregating BTM Energy Storage Devices

M. Parandehgheibi, S. A. Pourmousavi, Kiyoshi Nakayama, and Ratnesh K. Sharma
Conference Paper In Proc. of the IEEE PES General meeting, Chicago, USA, July 16-20, 2017

Abstract

In this paper, a two-layer controller is proposed to aggregate a fleet of behind-the-meter (BTM) energy storage devices based on the Transactive Energy (TE) concept. In the proposed model, aggregator offers an incentive to consumers to purchase power from and/or sell the excess power back to the grid. To do so, controller at the aggregator’s side determines optimal incentive which has to be offered to consumers by maximizing its own profit. Then, local controller at the consumer’s location optimizes battery operation by calculating purchased/sold power from/to the grid based on the local demand, PV generation, retail time-of-use (ToU) prices and demand charge, and the incentive offered by the aggregator to maximize its own profit. Different optimization problems are formulated in the two layers, and the profit of aggregator and consumers in the day-ahead energy market under perfect and imperfect prediction scenarios are compared.

BSS sizing and economic benefit analysis in grid-scale application

Shankar Mohan, Ali Hooshmand, S. A. Pourmousavi, and Ratnesh K. Sharma
Conference Paper In Proc. of the IEEE ISGT–North America Conference, Minneapolis, USA, 2016

Abstract

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 novel algorithm to integrate battery cyclic and calendar agings within a single framework

S. A. Pourmousavi, Babak Asghari, and Ratnesh Sharma
Conference Paper In Proc. of the IEEE ISGT–North America Conference, Minneapolis, USA, 2016

Abstract

Cyclic and Calendar agings are the two primary sources of degradation in a battery. An accurate battery degradation model can only be achieved when both processes are considered. In this paper, a novel framework is proposed to integrate Cyclic and Calendar aging processes. The proposed framework is able to accommodate different individual Cyclic and Calendar aging models only with slight modifications. It also can work conveniently as a universal degradation framework in different applications, such as large-scale battery storage systems in microgrids (MGs) and electric vehicles (EVs).

LFC model for multi-area power systems considering dynamic demand response

S. A. Pourmousavi, Mahdi Behrangrad, M. Hashem Nehrir, and Ali Jahanbani Ardakani
Conference Paper In Proc. of the IEEE Transmission and Distribution Conference, Dallas, USA, 2016

Abstract

Dynamic demand response (DR) is an integral part of ancillary services markets. The integration of dynamic DR control loop into the conventional load frequency control (LFC) model is presented by the authors in [1] . Extensive analytical analyses were carried out on single-area power system in previous study. In this paper, the idea is expanded to a general multi-area interconnected power system. Then, impacts of the proposed LFC-DR on the dynamic performance of the multi-area power systems in different conditions are simulated. Simulation results show a superior performance of the LFC-DR model for different conditions and power system models.

Optimal sizing and allocation of residential photovoltaic panels in a distribution network for ancillary services application

R. Ahmadi Kordkheili, S. A. Pourmousavi, J.R. Pillai, H.M. Hasanien, B. Bak-Jensen, and M. Hashem Nehrir
Conference Paper In Proc. of the International Conference on Optimization of Electrical and Electronic Equipment (OPTIM 2014), Romania, Feb. 2014

Abstract

Tremendous penetration of renewable energy in electric networks, despite its valuable opportunities, such as balancing reserve and ancillary service, has raised concerns for network operators. Such concern stems from grid operating conditions. Such huge penetration can lead to violation in the grid requirements, such as voltage and current limits. This paper proposes an optimization method for determining the number of photovoltaic (PV) panels together with their arrangement in the grid in order to maximize ancillary service, without violating grid operation limits. The proposed optimization method is based on genetic algorithm. To do so, single-objective and multi-objective optimization have been considered. The proposed method is implemented on a model of a part of a Danish distribution grid to verify its effectiveness. The simulation results prove the viability of the method, while keeping the grid requirements within standard operating limits.

The application of demand response for frequency regulation in an islanded microgrid with high penetration of renewable generation

K. Marchese, S. A. Pourmousavi, and M. Hashem Nehrir
Conference Paper In Proc. of the 2013 North American Power Symposium, Kansas city, USA, 2013, pp. 1-6

Abstract

Increasing the penetration of variable generation sources, such as wind and solar, potentially threatens the stability of the power systems. Past studies have shown that 15-20% renewable penetration is the largest amount the power systems can handle using conventional control. To overcome the challenge that variable renewable generation presents, this paper proposes that real-time demand response (DR) be used for ancillary services (AS). The impact of varying the amount of DR on the performance of a microgrid-configured distribution feeder is evaluated in this study. Simulation results have shown that a proper amount of DR resources can help to achieve higher penetrations of renewable generation while maintaining the desired system frequency.

Impact of high penetration of PV generation on frequency and voltage in a distribution feeder

S. A. Pourmousavi, A.S. Cifala, and M.H. Nehrir
Conference Paper In Proc. of the 2012 North American Power Symposium,Urbana-Champaign, USA, 2012, pp. 1-8

Abstract

This paper presents an evaluation of the impact of various levels of photovoltaic (PV) power penetration in a distribution feeder connected to a simplified grid model (SGM). PV generation is implemented in second-by-second iterations with power output based on actual solar radiation and air temperature data. High penetration levels of intermittent PV generation (15% and 30%) are employed in a feeder-configured microgrid to evaluate grid frequency and voltage characteristics. In this study, only governor droop control is included in the proposed SGM without the secondary control action (known as load frequency control). Two different grid models (fast and slow grid), PV generation configurations (concentrated and distributed), and PV penetration levels (15% and 30%) are considered in the simulation studies. Simulation results indicate the impact of the aforementioned parameters on the system frequency and voltage. Results also reveal that distributed PVs in a wide geographical area with different weather regime have less impact on the frequency and voltage.

Technology selection and unit sizing for a combined heat and power microgrid: comparison of DER-CAM and HOMER application programs

A.J. Litchy, C. Young, S. A. Pourmousavi, and M.H. Nehrir
Conference Paper In Proc. of the 2012 North American Power Symposium,Urbana-Champaign, USA, 2012, pp. 1-8

Abstract

The purpose of this paper is to design an optimal CHP islanded microgrid, through technology selection and unit sizing software, to be used for further research on real-time energy management. Two software packages, HOMER and WebOpt, originally developed at the National Renewable Energy Laboratory (NREL) and Lawrence Berkley Laboratory (LBL), respectively, are utilized. Using these programs, different cases are created and compared to justify the selected technologies and their respective prices. The final microgrid design contains renewable and alternative energy generation, hydrogen as an energy carrier, and electric storage.

Real-time optimal demand response for frequency regulation in smart microgrid environment

S. A. Pourmousavi and M.H. Nehrir
Conference Paper In Proc. of the 11th IASTED PES (EuroPES2012), Naples, Italy, 2012

Abstract

Real-time demand response (DR) in smart μgrid has been shown to be an effective tool for frequency regulation with increased penetration of renewable energy resources into the grid. Since DR is recognized as an incentive or direct payment to the participants, it is consequently desired to minimize the cost of DR for the utility. This paper presents an optimal DR strategy for minimizing the cost of DR for the utility in smart grid era. The economic model developed by Pennsylvania/New Jersey/Maryland (PJM) utility in the USA is used on an IEEE 13-bus standard system. Simulation results verify the effectiveness of the proposed approach to minimize the cost of DR for the utility. It is also shown that the DR, with or without optimization, decreases the overall cost of frequency regulation for the utility compared to the conventional spinning reserve, without sacrificing system stability.

A framework for real-time power management of a grid-tied microgrid to extend battery lifetime and reduce cost of energy

S. A. Pourmousavi, Ratnesh K. Sharma, and Babak Asghari
Conference Paper In Proc. of the 3rd IEEE PES Conference on Innovative Smart Grid Technologies (ISGT 2012), Washington D.C., 2012, pp. 1-8

Abstract

Because of different technical and economical concerns, battery is happened to be an inevitable part of a microgrid as well as the most expensive component. This fact brings up the necessity of a real-time power management to guarantee the maximum possible battery lifetime based on the final cost of energy. In this way, this study attempts to present a real-time management framework for a grid-tied microgrid based on battery life and cost estimation. In order to verify the effectiveness of the proposed framework, a grid-tied commercial microgrid, which is equipped with wind turbine, PV solar panels and Li-Ion battery package, is optimally sized by HOMER and dynamic models of different components have been developed in MATLAB/Simulink. Then, simulation study has been carried out for a year on the system. All data such as load demand, wind, temperature, solar radiation, and time-based electricity tariff are grasped from different places for a year. Results show that the proposed framework effectively extends the battery lifetime while slightly decreases the cost of energy for customer.

Providing ancillary services through demand response with minimum load manipulation

S. A. Pourmousavi, M.H. Nehrir, and C. Sastry
Conference Paper In Proc. of the IEEE 43rd North American Power Symposium (NAPS 2011), Boston, MA, 2011, pp. 1-6

Abstract

This paper presents a demand response (DR) algorithm for regulating system frequency using responsive customer loads, while minimizing the amount of manipulated loads. The dynamic model for a small islanded microgrid and an improved hill climbing controller are developed in MATLAB/Simulink to show the proof of concept. Simulation results show that the improved DR control strategy provides frequency and voltage regulation while minimizing the amount of manipulated responsive loads. As a result, customer quality-of-service (QoS) is not compromised, while a higher percentage of responsive loads (more non-spinning reserve) would be available for additional control for responding to unexpected disturbances.

Demand response for smart microgrid: initial results

S. A. Pourmousavi and M.H. Nehrir
Conference Paper In Proc. of the IEEE PES Conference on Innovative Smart Grid Technologies (ISGT 2011), Anaheim, CA, 2011, pp. 1-6

Abstract

This study is an attempt to address the frequency and voltage regulation inside of an islanded microgrid. Central demand response along with an adaptive hill climbing methodology is applied to a small islanded microgrid powered by a diesel generator. All dynamic models are developed in MATLAB/Simulink. Simulation results show that the proposed method has the potential to suppress the frequency variations and stabilize the voltage of the microgrid.

Towards real-time microgrid power management using computational intelligence methods

C.M. Colson, M.H. Nehrir, and S. A. Pourmousavi
Conference Paper In Proc. of the IEEE PES General Meeting, Minneapolis, MN, 2010, pp. 1-8

Abstract

Microgrids are an emerging technology which promises to achieve many simultaneous goals for power system stakeholders, from generator to consumer. The microgrid framework offers a means to capitalize on diverse energy sources in a decentralized way, while reducing the burden on the utility grid by generating power close to the consumer. As a critical component to enabling power system diversity and flexibility, microgrids encompass distributed generators and load centers with the capability of operating islanded from or interconnected to the macrogrid. To make microgrids viable, new and innovative techniques are required for managing microgrid operations given its multi-objective, multi-constraint decision environment. In this article, two example computational intelligence methods, particle swarm optimization (PSO) and ant colony optimization (ACO), for application to the microgrid power management problem are introduced. A mathematical framework for multi-objective optimization is presented, as well as a discussion of the advantages of intelligent methods over traditional computational techniques for optimization. Finally, a three-generator microgrid with an ACO-based power management algorithm is demonstrated and results are shown.

A new ANN-based methodology for very short term wind speed prediction using Markov chain approach

S. A. Pourmousavi and G.H. Riahy
Conference Paper In Proc. of the Electrical Power & Energy Conference 2008 (EPEC 2008), October 6-7, Vancouver, BC, Canada, 2008

Abstract

Since 2000, the increase of the installed wind energy capacity all over the world (mainly in Europe and United States) attracted the attention of electricity companies, wind farm promoters and researchers towards the short term prediction, mainly motivated by the necessity of integration into the grid of an increasing 'unknown' (fluctuating) amount of wind power. Besides, in a deregulated system, the ability to trade efficiently, make the best use of transmission line capability and address concerns with system frequency, accurate very short-term forecasts are motivated more than ever. In this study, very short term wind speed forecasting is developed utilizing Artificial Neural Networks (ANN) in conjunction with Markov chain approach. Artificial neural networks predict short term values and the results are modified according to the long term patterns due to applying Markov chain. For verification purposes, the integrated proposed method is compared with ANN. The results show the effectiveness of the integrated method.

Very short-term wind speed prediction using linear regression among ANN and Markov chain

S. A. Pourmousavi, S.M. Mousavi, A. Kashefi Kaviani, and G.H. Riahy
Conference Paper In Proc. of the International Conference on Power System Analysis, Control and Optimization (PSACO-2008), March 13-15, India, 2008

Abstract

The growing revolution in wind energy encourages for more accurate models for wind speed forecasting. In this study, a new integrated approach, which contains ANN, Markov chains and linear regression, is used due to very short-term prediction of wind speed. In this method, First ANN is used for primary prediction of wind speed. Then, second-order Markov chain is applied to calculate transition probability matrix for predicted wind speed in the first step. Finally, a linear regression among ANN primary prediction and calculated probability with Markov chain is used for the final prediction .The results of proposed method in comparison with the ANN results shows lower error of wind speed prediction particularly in the case of higher prediction horizons .The results are based on real wind speed data in an area of Denmark with 2.5 second resolution.

A new integrated approach for very short-term wind speed prediction using wavelet networks and PSO

E. Safavieh, A. Jahanbani Ardakani, A. Kashefi Kaviani,S. A. Pourmousavi, S. H. Hosseinian, and M. Abedi
Conference Paper In Proc. of the International Conference on Power System (ICPS2007), Dec. 12-14, India 2007

Abstract

Very short term wind speed forecasting is necessary for wind turbine control system. In this study, a new integrated approach using Wavelet-based networks and PSO is proposed for very short term wind speed forecasting. PSO algorithm is used for training a Wavelet networks and the whole integrated approach is applied for wind speed prediction. As a case study, the wind speed data from a site in Denmark with 2.5 s measured resolution is used for training and test of the network. Proposed approach is compared to multi layer perceptron networks with Back Propagation training algorithm. Results show that the new approach improve Mean Absolute Percentage Error (MAPE) and Maximum error of prediction.

Siting and sizing of distributed generation for loss reduction

A. Jahanbani Ardakani, A. Kashefi Kaviani,S. A. Pourmousavi, S. H. Hosseinian, and M. Abedi
Conference Paper In Proc. of the International Conference on Power System (ICPS2007), Dec. 12-14, India 2007

Abstract

The introduction of distributed generation (DG) onto distribution networks has a significant effect on losses. This effect cannot be characterized as a detrimental or beneficial but is dependent on the allocation of DG on each distribution network. This paper proposes a new method to calculate the optimal size and to identify the corresponding optimal location for DG placement (allocation) for minimizing the total power losses in distribution networks. The proposed (presented) algorithm is an evolutionary algorithm named Particle Swarm Optimization (PSO). The method is implemented and tested on a sample distribution network. The results show the importance of placement of DGs for minimizing losses.

Short-term wind speed prediction using MLP Neural Networks trained by PSO algorithm in wind turbine applications

A. Kashefi Kaviani,S. A. Pourmousavi, A. Jahanbani Ardakani, and G. H. Riahy
Conference Paper In Proc. of the 22nd International Power System Conference (PSC’07), Nov. 19-21, Tehran, Iran 2007 (IN FARSI)

Abstract

In this paper a new method for wind prediction in wind turbine application is proposed. Considering increase in share of electric power generation from wind across the world and random variations in wind, it is so vital to predict wind speed in different ranges. In this study, a multilayer perceptron artificial neural network trained by Particle Swarm Optimization (PSO) is used for wind speed prediction. Aperiodic and stochastic structure of wind causes those conventional training methods which use gradient tools could not train the network properly. In the other hand, the objective of training a neural network is finding weights and biases so that minimizes the training error. Hence, we can approach problem of training neural networks as an optimization problem. Since wind prediction using ANN is a sophisticated and nonlinear function, use of optimization methods is considered. The proposed method is applied on filtered and real wind data. Filtering ignores improper frequencies in wind frequency spectrum that will be eliminated in wind turbine blades.

Annual electricity demand prediction for Iranian agriculture sector using ANN and PSO

S. A. Pourmousavi and Nima Farrokhzad Ershad
Conference Paper In Proc. of the IEEE Congress on Renewable and Alternative Energy Resources (EPC’07), Oct. 25-26, Montreal, Canada 2007

Abstract

In this study, we used PSO algorithm and ANN to predict annual electricity consumption in Iranian agriculture sector. The economic indicators used in this paper are price, value added, number of customers and consumption in the previous periods. To predict the future values, a linearlogarithmic model of electrical energy demand is considered. The PSO algorithm applied in this study has been tuned for all its parameters and the best coefficients with minimum error are identified, while all parameter values are tested concurrently. Consumption in the previous periods has been used for testing estimated model. The estimation errors of PSO algorithm are less than that of estimated by genetic algorithm and regression method. In addition, ANN is used to forecast each independent variable and then electricity consumption is forecasted up to year 2010. Electricity consumption in Iranian agriculture sector from 1981 to 2005 is considered as the case for this study.

Multi-layer Artificial Neural Networks’ training using PSO algorithm

A. Kashefi Kaviani, S. A. Pourmousavi and A. Jahanbani Ardakani
Conference PaperIn Proc. of the 1st Joint Congress on fuzzy and Intelligent Systems (FIS’07), Aug. 30-31, Mashhad, Iran 2007 (IN FARSI)

Abstract

The main object of training of artificial neural networks is founding weights and biases so that minimize training error. Hence we can approach problem of ANN training as an optimization problem. Conventional methods for ANN training uses back propagation algorithm and other gradient algorithms. When the target function is severely nonlinear and sophisticated, the conventional methods has a lot of weak points. Using PSO algorithm in ANN training and comparing with back propagation algorithm shows that in sophisticated problems, the new algorithm has higher performance. At the end, new algorithm is applied on two problems and results of comparison with back propagation algorithm is presented.

Dynamic modeling and simulation of a PEMFC for DG Applications

Nima Farrokhzad Ershad and S. A. Pourmousavi
Conference PaperIn Proc. of the 6th Iranian Energy Symposium, Jul. 23-25, Tehran, Iran 2007 (IN FARSI)

Abstract

This paper describes dynamic modeling and simulation results of a Hybrid Wind-PEM fuel cell System. The system consists of a proton exchange membrane fuel cell (PEMFC),a Wind turbine, ultracapacitors, an electrolyzer, and a power converter. The output fluctuation of the load voltage due to load variation is reduced using a fuel cell stack. At mid-night, when the amount of demand is low, excess energy form Wind Turbine is converted to hydrogen using an electrolyzer for later use in the fuel cell. Ultracapacitors and a power converter unit are proposed to minimize voltage fluctuations in the system and generate AC voltage. Dynamic modeling of various components of this small isolated system is presented. Dynamic aspects of temperature variation and double layer capacitance of the fuel cell are also included. PID type controllers are used to control the fuel cell system. MATLAB/SIMULINK is used for the simulation of this highly nonlinear energy system. System dynamics are studied to determine the voltage variation throughout the system. Transient responses of the system to step changes in the load current are presented. Analysis of simulation results and limitations of the fuel cell energy system are discussed. The voltage variation at the output was found to be within the acceptable range. The proposed system does not need conventional battery storage. It may be used for off-grid power generation in remote communities.