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A data-driven approach to estimate battery cell temperature using NARX neural network model

Md Mehedi Hasan, S. Ali Pourmousavi, and Tapan K. Saha
Journal PaperSubmitted for review to the Electric Power Systems Research, Apr. 14, 2019

Abstract

Battery cell temperature is a key parameter in battery life degradation, safety and dynamic performance. Intense charging-discharging operation and high ambient temperature escalate battery cell temperature, which accelerates its degradation. Therefore, accurate battery cell temperature estimation can play a significant role in ensuring optimal operation of a battery energy storage system (BESS) considering its degradation. In order to estimate battery cell temperature as accurate as possible, non-linear models are necessary because of the non-linear nature of the battery operation. The main objective of this paper is to propose a data-driven model based on a Non-linear Autoregressive Exogenous (NARX) neural network to estimate battery cell temperature in a utility-scale BESS, considering strongly correlated independent variables (i.e., charging-discharging current and ambient temperature). Due to different temperature and weather characteristics in each season, seasonal NARX model has also been derived and compared with the universal one. The proposed model has been evaluated using field data collected from a grid-connected BESS within a PV plant. The simulation results show high accuracy of the model compared to the measured data for both seasonal and universal models without considering the complexity of the large-scale battery and container thermal dynamics. In particular, more than 95% of the time, the estimated values yield root mean squared errors (RMSE) below 1C in different conditions, which confirms the validity and accuracy of the model. Moreover, seasonal models show better performance with 18% to 50% less RMSE on average (for 1 hour to 24 hours ahead estimation) compared to the universal model.

An analytical approach for sizing of energy storage for inertial support and primary frequency regulation

Umer Akram, N. Mithulananthan, Rakibuzzaman Shah, and S. Ali Pourmousavi.
Journal PaperSubmitted to the IEEE Transactions on Sustainable Energy, March 24, 2019

Abstract

The replacement of conventional synchronous generators by renewable energy sources reduce the mechanical inertia of power systems. Reduction in the system mechanical inertia may jeopardize reliable and stable operation, especially in restoring frequency following a disturbance in a power system with high penetration of renewable energy. Energy storage can be utilized in such situations for providing inertial support and frequency regulation. Several energy storage technologies are available in market and each offers different power, energy, and economic characteristics and they are subjected to different limitations, including cost. Thus, it is crucial to find out the optimum storage technology for given power system. In this paper, an analytical technique is developed to estimate the size of the storage system to provide inertial response (IR) and primary frequency regulation (PFR) in power systems with high penetrations of renewable energy sources. Different types of storage technologies including hybrid storages suitable for IR and PFR services are sized while considering their dynamic characteristics and limitation. An economic analysis is also carried out to find the optimum storage technology for IR and PFR services. The theoretical estimated results are compared and corroborated by non-linear simulations.

On the battery efficiency and degradation modelling in sizing and management studies

S. Ali Pourmousavi and Tapan K. Saha.
Journal PaperSubmitted to the Electric Power Systems Research, March 8, 2019

Abstract

Battery energy storage systems (BESS) are going to be an inevitable part of the smart grids. As a result, different aspects of the BESS operation and application have been investigated in numerous papers in recent years, e.g., optimal sizing and long-term operation. In such studies, different formulations have been used for battery efficiency during charging and discharging, which leads to different sets of results. In addition, most of the battery sizing studies ignored degradation over its useful lifetime. In this paper, we classify various battery efficiency formulations and compare their performance through an extensive simulation study. Moreover, a linear cyclic and calendar ageing formulation is proposed based on counting energy throughout and idle time that can be conveniently integrated with any battery optimal sizing and long-term management algorithms. The simulation results reveal a significant impact of degradation on the optimal solutions obtained in sizing studies.

Feasibility study of a P2P energy trading algorithm in a grid-tied power network

M Imran Azim, S. Ali Pourmousavi, Wayes Tushar, and Tapan K. Saha
Conference PaperAccepted for presentation in the IEEE PES General Meeting, Atlanta, USA, Aug. 4-8, 2019

Abstract

This paper studies the feasibility of peer-to-peer(P2P) energy trading in a grid-tied network. The main objectives are to understand the impact of a P2P energy trading model onthe network operation, and thus demonstrate the importance of taking various issues related to power network into account while designing a P2P trading scheme. To do so, firstly a simple mechanism is developed for energy trading among prosumers without considering any network constraints, as done by many existing studies. Once the trading outcome is finalised, the developed scheme is tested on the low-voltage (LV) network model to check its feasibility in real world. It is shown that while the considered trading scheme is economically beneficial to the prosumers compared to the current incentive mechanisms (such as feed-in-tariff), 1) it could be unfit for real deployment due to violating voltage limits in some scenarios, and 2) the grid may experience financial losses for compensating the path losses during P2P trading at a given interval.

Analysis of rebound effect modelling for flexible electrical consumers

Giulia De Zotti, Daniela Guericke, S. Ali Pourmousavi, Juan Miguel Morales, Henrik Madsen, and Niels K. Poulsen.
Conference PaperAccepted for presentation in the IFAC Workshop on Control of Smart Grid and Renewable Energy Systems, Jeju, Republic of Korea, June 10-12, 2019

Abstract

Demand response (DR) will be an inevitable part of the future power system operation to compensate for stochastic variations of the ever-increasing renewable generation. A solution to achieve DR is to broadcast dynamic prices to customers at the edge of the grid. However, appropriate models are needed to estimate the potential exibility of different types of consumers for day-ahead and real-time ancillary services provision, while accounting for the rebound effect (RE). In this study, two RE models are presented and compared to investigate the behaviour of flexible electrical consumers and quantify the aggregate exibility provided. The stochastic nature of consumers' price response is also considered in this study using chance constrained (CC) programming.

Battery cell temperature estimation model and cost analysis of a grid-connected PV-BESS plant

Md Mehedi Hasan, S. Ali Pourmousavi, and Tapan K. Saha.
Conference PaperAccepted for presentation in the IEEE PES ISGT Asia, Chengdu, China, May 21-24, 2019

Abstract

Battery cell temperature is an important factor in battery capacity degradation, performance, and safety. Elevated battery cell temperature, due to intense battery operation with high charging-discharging current and ambient temperature, accelerates battery capacity degradation as well as causing extra cooling cost. Therefore, it is indispensable to estimate battery cell temperature accurately for optimal BESS operation considering capacity degradation and its associated costs. The main objectives of this paper are to propose a linear model to estimate battery cell temperature using Autoregressive Integrated Moving Average with eXogenous variables (ARIMAX) considering strongly correlated independent variables and propose a cost function of PV plant with and without battery operation. The simulation results using field data show high accuracy of the proposed temperature estimation model without considering the complex thermal dynamics of the entire system. In addition, comprehensive cost functions are developed to show the benefit of integrating battery storage into a PV plant and determine influential factors to consider in any optimal battery operation systems.

Consumers' flexibility estimation at the TSO level for balancing services

Giulia De Zotti, S. Ali Pourmousavi, Juan M. Morales, Henrik Madsen, and Niels K. Poulsen
Journal PaperIEEE Transactions on Power Systems, 34(3):1918-1930, 2019

Abstract

  • what is the problem?
    Using consumers' flexibility by the TSO is a way to provide extra AS required to accommodate higher level of renewable generation. This can be done by a time-varying prices submitted to flexible loads. However, effective models are needed for the TSO to generate price signal.
  • why is it interesting?
    Larger renewable generation in the power system is not going to have without providing enough flexibility for safe operation of the system. Demand flexibility resources are free of charge, available at all times, and will be achievable with minimum upfront cost of infrastructure. An efficient and detail model to estimate consumers' reaction to price signals is necessary to achieve this.
  • what is the approach?
    We formulated consumers' flexibility as an optimisation problem for rational end-users. Consumers willingness to respond to price signals are modelled stochastically and actual aggregated load data are used for 29 categories of load demand. The deterministic optimisation model was converted to a chance-constrained program to account for the stochasticities.
  • what is new?
    The proposed formulation was new in essence and including different load categories to solve the problem was unique. Converting the deterministic problem into a stochastic one was offered to estimate load flexibility considering the confidence level.
  • How was it tested?
    Optimisation model was implemented in GAMS and the problem was solved in MATLAB by calling GAMS model using CPLEX solver. The normality assumption of the stochastic terms are tested and proved, and the problem was solved for two confidence levels.

A control-based method to meet TSO and DSO ancillary services needs by flexible end-users

Giulia De Zotti, S. Ali Pourmousavi, Juan M. Morales, Henrik Madsen, and Niels K. Poulsen
Journal PaperSubmitted to the IEEE Transactions on Power Systems, Dec. 23, 2018

Abstract

This paper presents a new methodology to exploit consumers’ flexibility for the provision of ancillary services (AS). The proposed framework offers a control-based approach that adopts price signals as the economic driver to modulate consumers’ response. In this framework, various system operators broadcast price signals independently to fulfil their AS requirements. Appropriate flexibility estimators are developed from the transmission system operator (TSO) and distribution system operator (DSO) perspectives for price generation. An artificial neural network (ANN) controller is used for the TSO to infer the price-consumption reaction from pools of consumers in its territory. A PI controller is preferred to represent the consumers’ price-response and generate time-varying electricity prices at the DSO level for voltage management. A multi-timescale simulation model is built in MATLAB to assess the proposed methodology in different operational conditions. Numerical analyses show the applicability of the proposed method for the provision of AS from consumers at different levels of the grid and the interaction between TSO and DSOs through the proposed framework.

Variability, Scalability, and Stability of Microgrids - Chapter 2: Microgrid Control Overview

S. Ali Pourmousavi, F. Shahnia, M Imran Azim, Md. Asaduzzaman Shoeb, and G.M. Shafiullah.
Book ChapterSubmitted to the editors, IET Press, Nov. 1, 2018

Abstract

To be included later ...

Improving predictability of renewable generation through optimal battery sizing

S. Ali Pourmousavi, P. Wild, and Tapan K. Saha
Journal PaperAccepted for publication in the IEEE Transactions on Sustainable Energy, Nov. 23, 2018

Abstract

  • what is the problem?
    Renewable energies such as PV and wind are unpredictable by nature to a large extent. Despite all the new forecasting methods, they still conservatively bid in the market and pay penalty to the ancillary services market based on causer pay.
  • why is it interesting?
    If there is a way that the predictability of renewable generation can be improved, it helps plant operators as well as bring larger economic benefits to the plant owners. It would be more interesting if the proposed approach can be prediction-method-agnostic.
  • what is the approach?
    An optimal battery sizing methodology is proposed to improve renewable generation predictability using “Seasonal-Trend decomposition based on LOESS1 (STL)” decomposition technique, self-similarity estimation, and enhancing it through filtering.
  • what is new?
    The idea of improving predictability and using the self-similarity concept in this context was offered for the first time. In addition, the STL decomposition technique application in this study was new in the power system engineering community. Moreover, the battery degradation over its useful lifetime was added to the optimisation problem.
  • How was it tested?
    The optimisation problem is solved using Gurobi in MATLAB. The battery sizes are examined in terms of power magnitude and battery SOC limits using the test dataset. Then, predictability improvement was tested by developing four prediction methods for three different horizons. Finally, the cost-effectiveness of the battery application was examined by standard LCOE calculation against average AS market prices.

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 CIGRE Conference, Paris, August 26-31, 2018

Abstract

  • what is the problem?
    Batteries are inevitable in the future power system. At the same time, different battery technologies are under development and lots of unknown exist to the research and engineering communities about their operation. In addition, the benefit and technical limits of operating batteries next to a PV plant is relatively unknown in the research and industry communities.
  • why is it interesting?
    It is interesting to see a comprehensive analyses of the battery operation within a relatively large PV plant in terms of operation and technical constraints. It provides insights into the hybrid system operation and offers a wide range of learnings to create better systems of the same type in the future.
  • what is the approach?
    In this paper, statistical analyses are carried out based on two years of 600 kW/760 kWh Li-Polymer battery operation within the UQ Gatton Solar Research Facility with 3.275 MWp of PV. In addition, the performance of different PV tracking technologies are evaluated based on field data to reveal the overall yield of the plant.
  • what is new?
    While there are numerous simulation studies and small-scale field data analysis for performance evaluation of storage and PV systems, there is no such a study based on real data for a utility-scale PV and battery plant. It provides valuable insights into the system operation and performance over the years.
  • How was it tested?
    The entire study is done using actual data from the UQ Gatton Solar Research Facility. Different statistical tools are used to draw insights from the real operation.

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 ENERGYCON Conference, Limassol, Cyprus, June 3-7, 2018

Abstract

  • what is the problem?
    The future power system should accommodate large amount of variable renewable generation in order to achieve renewable targets. Also, using conventional generators to provide ancillary services in the system should be limited to special circumstances as a way to decrease emissions from power plants by increasing conventional generators efficiencies.
  • why is it interesting?
    The most cost-effective way to provide such solutions is to use the resources that exist. Storage of different kinds are still very expensive. Conventional generators contribute to emission and should be prohinited. Therefore, an alternative solutions are needed to solve the problem.
  • what is the approach?
    A control-based ancillary services method is proposed that can exploit the load demand flexibility in order to provide ancillary services for power system operation. It allows multiple system operators to fulfil their requirements simultaneously and seamlessly at all times.
  • what is new?
    Different alternative approaches including P2P and Transactive Energy (TE) are introduced and compared in this paper along with an introduction to the AS4.0 approach.
  • How was it tested?
    It is a conceptual paper that hypothesized alternative approaches. There is no simulation study in the paper.

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

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

Abstract

  • what is the problem?
    Application of EVs in providing services to the grid through aggregator depends on the economic benefits of different stakeholders. Therefore, developing appropriate aggregation algorithms that deal with uncertainties involved in the process becomes very important.
  • why is it interesting?
    From power system operation point of view, EVs are mobile storage that can be used for the benefit of power system operation. Thousands of EVs can provide substantial amount of storage without upfront costs to the system operator.
  • what is the approach?
    An optimisation formulation is developed considering undertainty in the market prices and EVs availability considering battery degradation cost. Then, the problem is solved as a two-stage stochastic programming to find the best bids for the aggregator's participation in the market.
  • what is new?
    The bidding strategy has not been reported in the literature for EV aggregator. Also, considering the battery degradation cost in the formulation was new, and solving the problem as a two-stage stochastic programming offered a new perspective to solve the problem.
  • How was it tested?
    The optimisation problem was implemented in GAMS with day-ahead and real-time market mechanisms.

Learning from an operational utility-scale Li-Polymer battery system in a PV plant

S. Ali Pourmousavi, and Tapan K. Saha
Conference Presentation EECON Conference, April 05-06, 2018, Brisbane QLD, Australia

Abstract

Utility-scale energy storage is becoming the most viable and widespread solution to safely accommodate large-scale renewable generation without harming the power system operation. Different storage technologies have been considered for such applications, where various Li-based battery technologies gained the largest share of the market, after pump hydro storage. While numerous research studies addressed optimal operation of battery storage using mathematical modelling, there are few examples of battery performance analysis in such applications. The University of Queensland owns and operates one of the largest PV/battery research facility in the world at the Gatton campus. It includes 3.275 MWp solar PV modules with three different solar tracking technologies, and 600kW/760 kWh Li-Polymer battery system. The plant is primarily responsible for meeting the campus load demand, and the excess energy, if available, can be pushed back to the local network. This presentation will provide valuable insights into the utility-scale battery operation in connection with a PV plant and local network based on 1.5 years of actual data. The focus will be on evaluating the operation and performance of the battery system from different perspectives. I will start with briefly introducing the plant, battery system configuration, cooling mechanism, and inverter. Then, I will briefly review battery operation rules in the plant which is managed by a central supervisory controller. Battery operation and performance for reactive power support will be reviewed next. Afterwards, voltage agreement with the local network operator will be reviewed, and the impact of the battery will be quantified in that regard. Finally, the performance of the battery for demand charge management (or peak shaving), excess PV storage, PV ramp-rate control, and additional services to the local network will also be examined based on operational data. Data analysis will show that the battery operation had positive impacts on the plant operation.

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

S. Ali Pourmousavi, and Tapan K. Saha
Journal PaperSolar Energy, 166:242–254, 2018
  • what is the problem?
    Batteries are used (and will be used) in renewable generation plants to compensate quick variations in the output of these resources, among other things. It could be very useful for researchers and engineers to know the kind of regime that battery will experience in such applications in real-world condition beyond simulation studies.
  • why is it interesting?
    Having insights in to the different ways that battery is stressed during ramp-rate control mode can help to select suitable battery for the application, design better battery kWh and kW sizes more realistically, and develop energy management systems for battery operation which account for the real-wrold conditions.
  • what is the approach?
    In this paper, one year of operational data of a 600 kW/760 kWh Li-Polymer battery during ramp-rate control mode of a 3.275 MWp PV plant at the UQ Gatton campus are analysed. Maximum, minimum, average, standard deviation, skewness, and kurtosis are calculated for different parameters while statistical models are derived based on the given data.
  • what is new?
    The insights offered to a utility-scale battery experience operating in a PV plant are unique in this paper. Battery energy, power, SOC, cell temperature, and time difference between two consecutive events are among the parameters that have been evaluated. In addition, technical and operational values of a super-capacitor in such a system is hypothesized. Moreover, the impact of PV inverter operation on the ramping events on the DC and AC sides are analysed which resulted in very important observations.
  • How was it tested?
    This paper is developed based on actual data of a battery system in ramp-rate control mode. So, there is no test required in this kind of study.

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 IEEE Access, 6:11694-11706, 2018
  • what is the problem?
    Existing Ancillary Services (AS) market and its alternative approaches are not suitable for the future power systems with large amount of renewable energy. They are slow, linear, and limited to a specific power system functionality.
  • why is it interesting?
    This is an important problem for wider use of renewable generation. They are variable and unpredictable in nature, which makes it difficult to deal with in real-time operation. Larger penetration of power systems with the existing AS mechanism is not possible.
  • what is the approach?
    The AS 4.0 is an alternative to the existing market-based approaches, which utilises real-time pricing mechnism along with control principles to provide services to the grid from any possible flexibility resources. The proposed method redefines AS problem into space and time for a comprehensive solution.
  • what is new?
    It is a holistic change in the existing AS provision mechanism. The proposed method can accommodate AS provision at the different time and space. It also utilise any flexibility potential within the network. It also can be tied up with other energy carriers easily. Moreover, the proposed approach respect users' privacy to an unprecedented level.
  • How was it tested?
    The AS 4.0 is still an idea. This paper lays out a general framework to define the solution. It also provides a comprehensive review of the existing AS mechanisms in comparison to the proposed method.

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.

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.

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

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.

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. Ali 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. 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

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. 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

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. Ali 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. 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

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. Ali 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. Ali 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. 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

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. 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

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. 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

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. Ali 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. 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

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. 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

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. 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

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. 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

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. 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)

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. 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

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. 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)

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. Ali 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.