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Ancillary services 4.0: A top-to-bottom control-based approach for solving ancillary service problems in smart grids

Giulia De Zotti, S. Ali Pourmousavi, Henrik Madsen, and Niels K. Poulsen
Journal Paper Accepted for publication in the IEEE Access, January 31, 2018


The future power system will experience a large amount of renewable generation with highly stochastic and partly unpredictable characteristics. This change in energy production will imply significant consequences related to the provision of Ancillary Services (AS). Current markets dedicated to the provision of AS are not able to benefit from the flexible energy resources. They also cannot cope with the new level of stochasticity, non-linearity and dynamics of generation and flexibility. To overcome such issues and exploit the potential of flexibility resources, a new strategy is required. In this paper, by capitalising on flexibility resource potential, the Ancillary Services 4.0 approach is proposed, which offers a comprehensive solution for the AS provision in the smart-grid era.

Evaluation of battery operation in ramp-rate control mode within a PV plant: A case study

S. Ali Pourmousavi, and Tapan K. Saha
Journal Paper Submitted for review to the Solar Energy, December 15, 2018


Spatial and temporal variability of PV generation is a challenge for secure operation of power systems. Several solutions are proposed to deal with this issue. Among the proposed solutions, storage technologies (particularly battery) attracted more attention as a promising solution for the application in medium- and large-scale PV plants. While numerous research studies addressed optimal sizing and real-time operation of storage systems in such applications, there is no study on battery operation assessment in real-world application based on field data. In this paper, one year of experimental data from a 3.275 MWp PV plant with 600kW/760kWh Li-Polymer battery system is examined from different perspectives (e.g., battery energy, power, rate of change of power, and state-of-charge (SOC)) to draw insights from battery operation within the plant. The field data inherently contains system-wide losses, smoothing effect of the PV plant, and PV inverter operation for reactive power control, which provides a realistic assessment. Furthermore, several operational parameters (such as energy and power during ramp events) are evaluated and modelled using appropriate statistical tools based on experimental data. In addition, a simple super-capacitor sizing study is carried out to reveal the effectiveness of a hybrid energy solution for such applications. Observations and insights drawn from analyses will help future research in battery sizing and operation studies to effectively account for real-world characteristics and requirements.

Learning from a 3.275 MW utility-scale PV plant project: Update and new remarks

S. Ali Pourmousavi, P. Wild, F. Bai, R. Yan, Tapan K. Saha, and D. Eghbal
Conference Paper Accepted for oral presentation to the CIGRE Conference, Paris, August 26-31, 2018


This paper presents thorough investigations of a utility-scale PV and battery system operation, connected to an 11 kV distribution feeder. The plant contains 3.275 MWp solar PV system and 600kW/760kWh battery storage, which is located at the University of Queensland Gatton campus and funded by the Australian Federal Government Education Infrastructure Fund. The PV plant consists of three different PV tracking technologies (namely fixed-tilt, single-axis tracking, and double-axis tracking) and state-of-the-art Li-Polymer battery system. An initial report on the performance and grid integration challenges of the plant of this size was presented in CIGRE 2016 Paris Session. Now we have more than two years of PV plant data along with one and a half years of battery operational data for analysis. Therefore, annual and seasonal operation and performance of the plant can be assessed effectively. This paper is organised in four sections. Section 1 outlines the context of the paper from a general perspective. Then, configuration of the PV and battery systems within the plant is briefly reviewed in section 2, where the focus is on the battery system configuration and operation algorithm. Section ‎3 offers in-depth analyses on the plant operation in terms of PV annual and seasonal production, battery operational performance, and voltage regulator operation from different perspectives. In particular, PV production is analysed in terms of annual performance and the impact of different tracking systems on the plant yield in subsection 3.1, which is followed by seasonal effect investigation. Battery operation in terms of energy, power, state-of-charge (SOC) level, and cell temperature is assessed in subsection 3.2 considering seasonal impact. In subsection 3.3, voltage at the point of common coupling (PCC) is analysed. Most notably, the impact of PV variations on the operation of step voltage regulator (SVR) is evaluated. Finally, the paper is concluded in section 4. Through these analyses, key observations are provided to better understand the plant performance and relevant network impacts.

Utilizing flexibility resources in the future power system operation: Alternative approaches

Giulia De Zotti, S. Ali Pourmousavi, Henrik Madsen, and Niels K. Poulsen
Conference Paper Accepted for presentation in the ENERGYCON Conference, Limassol, Cyprus, June 3-7, 2018


Future power system will experience large amount of renewable generation with highly stochastic and partly unpredictable characteristics. To safely operate power system, new Flexibility Resources (FRs) are needed to participate in the operation. Some of the new FRs 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 (IES) is needed to exploit such cross-sectoral opportunities. On the other hand, small FRs at the distribution level exist which can play an important role in the future. To exploit existing FRs, however, new operational strategies are needed. In this paper, Transactive Energy (TE) and Control-Based Approaches (CBA) are explained as the two mainstream frameworks in relation to the future energy system operation. The paper investigates benefits and drawbacks of each framework and finally defines a benchmark to better understand the potential of these solutions for the future energy management. The paper also concludes that more comprehensive operational approaches, beyond distribution system management, are required to fulfill the upcoming requirements.

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

Yelena Vardanyan, Frederik Banis, S. Ali Pourmousavi, and Henrik Madsen
Conference Paper Accepted for presentation in the ENERGYCON Conference, Limassol, Cyprus, June 3-7, 2018


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. Ali Pourmousavi, Feifei Bai, and Tapan Kumar Saha
Conference Paper In Proc. of the AUPEC, Melbourne, Australia, November 19-22, 2017
  • what is the problem?
    Large-scale batteries generate heat during charging and discharging events. So, we are interested to know the impact of charge/discharge regime on the generated heat. Also, it is quite important to quantify the impact of excessive heat in terms of battery degradation.
  • why is it interesting?
    Since batteries in large-scale application sit in an enclosed container, the generated heat should be removed as fast as possible. This will improve battery operation in terms of available capacity, available charge, and round-trip efficiency. To do so, active and passive cooling mechanisms are employed in such applications. However, excessive heat leads to over-consumption of the active cooling system. This will, in turn, reduce the overall efficiency of the plant.
  • what is the approach?
    In this study, we utilised operational data of a 600kW/760kWh battery within a 3.2MWp PV plant at the University of Queensland campus in Gatton, QLD, Australia. Through the data analyses, we show that both charging and discharging events increase battery temperature substantially. We also found a strong linear relationship between current and temperature of the battery during discharge events. Such as strong correlation has not been identified during charging incidents. We also found out that the extra battery degradation caused by the excessive heat is substantial.
  • what is new?
    This is the first study of its kind to show such impact for large-scale battery systems using real-world operational data. The linear relation between the temperature and current during discharging events is a remark with huge consequences which is made in this paper. The difference identified between charging and discharging regimes on battery temperature is yet another significant insight presented in this study.
  • How was it tested?
    First, we selected charge and discharge events where the ambient temperature had nothing to do with the temperature rise. Then, we developed several measures (such as Temperature Rising Slope, Peak Temperature, Absolute Temperature Change, Peak Current, Total Charge, and Temperature Rising Delay) to conduct analyses. After that, we tried to find a linear or piecewise linear relationship between the measures and battery temperature during the event. Finally, we used Zhurkov model quantify the extra degradation occurred due to excessive temperature.

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

Mousa Marzband, Masoumeh Javadi, S. Ali Pourmousavi, and Gordon Lightbody
Journal Paper Electric Power Systems Research, 157:187–199, 2018
  • what is the problem?
    Active distribution networks are the future of power system with prosumers. Currently, there is no market mechanism to encourage energy exchange among prosumers to increase competition with the utility companies.
  • why is it interesting?
    The prosumers can trade energy and ancillary services among each other, which reduce stress on the main power grid, losses, and cost of operation by facilitating local generation and consumption. It also helps retailers to defer upgrade in the system.
  • what is the approach?
    A game-theory market structure with multiple retailers, prosumers, and devices are developed. Load flexibility, storage, and dispatchabe and non-dispatchable resources are considered to participate in the market looking after their benefits. The proposed market structure encourage local generation and demand.
  • what is new?
    Considering three types of players and load flexibility is new in this study. Additionally, the Nikaido-Isoda Relaxation Algorithm (NIRA) is used to obtain global optimal solutions among all different players. Uncertain parameters are appropriately modelled with statistical techniques and taguchi0s orthogonal array testing (TOAT) is utilised to reduce number of scenarios for faster simulation.
  • How was it tested?
    A small active distribution system consisting of three home microgrids and two retailers are simulated. Three test cases are defined and simulated for comparison purposes. The proposed market structure outperformed the other scenarios significantly in different aspects.

A two-layer incentive-based controller for aggregating BTM energy storage devices

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


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.

Optimal battery sizing for behind-the-meter applications considering participation in demand response programs and demand charge reduction

Ali Hooshmand, S. Ali Pourmousavi Kani, Ratnesh K. Sharma, Shankar Mohan
Patent September 7 2017. US Patent App. 15/416,810


A system and method are provided. The system includes a processor. The processor is configured to receive power related data relating to power usage of power consuming devices at a customer site from a plurality of sources. The processor is further configured to generate object function inputs from the power related data. The processor is additionally configured to apply the generated object function inputs to an objective function to determine an optimal capacity for a battery storage system powering the power consuming devices at the customer site while minimizing a daily operational power cost for the power consuming devices at the customer site. The processor is also configured to initiate an act to control use of one or more batteries of the battery storage system in accordance with the optimal capacity for the battery storage system.

Resilient battery charging strategies to reduce battery degradation and self-discharging

S. Ali Pourmousavi Kani, Babak Asghari, Ratnesh K. Sharma
Patent August 10 2017. US Patent App. 15/363,876


Computer-implemented methods and, a system are provided. A method includes constructing by an Energy Management System (EMS), one or more optimization-based techniques for resilient battery charging based on an optimization problem having an EMS cost-based objective function. The one or more optimization-based techniques are constructed to include a battery degradation metric in the optimization problem. The method further includes charging, by the EMS, one or more batteries in a power system in accordance with the one or more optimization-based techniques.

Innovative framework combining cycling and calendar aging models

S. Ali Pourmousavi Kani, Babak Asghari, Ratnesh K. Sharma
Patent April 27 2017. US Patent App. 15/336,725


Aspects of the present disclosure describe a single battery degradation model and methods that considers both CYCLING and CALENDAR aging and useful in both energy management and battery management systems that may employ any of a variety of known battery technologies.

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

S. Ali Pourmousavi Kani, Babak Asghari, Ratnesh K. Sharma
Patent February 04 2016. US Patent App. 15/015,377


A method and system are provided. The method includes determining, by a processor, a set of battery aging modeling parameters that include battery capacity for a battery based on a statistical analysis applied to experiment data. The experiment data is obtained from measurements of a set of battery parameters that include battery capacity and that are taken by a hardware-based battery parameter monitoring device during a plurality of experiments which vary another set of battery parameters. The set and the other set have at least some different members. The method further includes generating, by the processor, a battery aging neural network based model for the battery that includes the set of battery aging modeling parameters. The method also includes storing the battery aging neural network based model in a memory device.

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. Ali Pourmousavi
Patent U.S. Patent 9,020,649 - 2015


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


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. Ali Pourmousavi, Mehdi Savaghebi, Josep M Guerrero, and Mohammad Hashem Nehrir
Journal Paper Energies, 9(1):34, 2016


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


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


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


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


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


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


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


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


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


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


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


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


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. Ali 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


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


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. Ali 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


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


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


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. Ali 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


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. Ali 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


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. Ali 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


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. Ali Pourmousavi
Conference Paper In Proc. of the IEEE PES General Meeting, Minneapolis, MN, 2010, pp. 1-8


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. Ali 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


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. Ali 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


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


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


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. Ali 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)


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. Ali 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


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. Ali 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)


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. Ali Pourmousavi
Conference PaperIn Proc. of the 6th Iranian Energy Symposium, Jul. 23-25, Tehran, Iran 2007 (IN FARSI)


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.