Abstract

Most literature surrounding optimal bidding strategies for aggregators in European day-ahead market (DAM) considers only hourly orders. While other order types (e.g., block orders) may better represent the temporal characteristics of certain sources of flexibility (e.g., behind-the-meter flexibility), the increased combinations from these orders make it hard to develop a tractable optimization formulation. Thus, our aim in this paper is to develop a tractable optimal offering strategy for flexibility aggregators in the European DAM (a.k.a. Elspot) considering these orders. Towards this, we employ a price-based mechanism of procuring flexibility and place hourly and regular block orders in the market. We develop two mixed-integer bi-linear programs: 1) a brute force formulation for validation and 2) a novel formulation based on logical constraints. To evaluate the performance of these formulations, we proposed a generic flexibility model for an aggregated cluster of prosumers that considers the prosumers’ responsiveness, inter-temporal dependencies, and seasonal anddiurnal variations. The simulation results show that the proposed model significantly outperforms the brute force model in terms of computation speed. Also, we observed that using block orders has potential for profitability of an aggregator.

Abstract

The ever-increasing uptake of distributed energy resources necessitates the introduction of local electricity markets at the residential level. Electric retailers, who are adversely affected by these changes, can make a profit by operating local trading platforms and offering services through community-level battery storage. In this work, we propose a Stackelberg game-based approach for sizing the centralized battery unit together with the operation of the multi-interval local market. The optimization is formulated as a bilevel program, where the leader is the market aggregator responsible for determining the local prices and battery charging/discharging schedules. Also, the followers in the bilevel program are prosumers, who can vary electricity consumption with respect to their comfort and cost of electricity. Upon obtaining the optimal capacity of the community storage, we modify the algorithm to efficiently operate the battery on a daily basis. The applicability of the proposed model is evaluated using real-world data of residential prosumers with rooftop photovoltaic systems for two different pricing schemes, which represents the profit trade-off between the aggregator and prosumers. The results show the profitability of the proposed model for community storage installation, where a relatively short payback period can be achieved on either pricing schemes.

Abstract

As the conventional AC power grid is evolving with the integration of renewable energy resources (PV and wind) and power electronic converters, we discover more technical and economic values for DC microgrids (MGs), particularly with electric vehicles (EVs) and battery energy storage systems (BESSs) changing the load landscape. This paper presents a double-layer centralized hierarchical control framework to achieve the DC bus voltage regulation and power coordination of a PV-BESSs based DC MG, which operates in fully islanding condition. The proposed control strategy consists of two levels: 1) The primary control can achieve the DC bus voltage regulation by modifying the commonly-used inner and outer PI control loops; 2) The secondary control considers the characteristics of different types of BESSs. The latter is also responsible for load sharing by generating power/current reference signals among different DGs and DC converters. The advantage of the proposed control strategy is to eliminate the droop control as well as the conventional secondary control, which is designed to restore the voltage deviation caused by droop equations in a three-level hierarchical control system. The simulation results show that the proposed control strategy is effective to stabilize the DC bus voltage and it can share the dynamic power variation within DC MG.

Abstract

Aggregators are expected to become an inevitable entity in future power system operation, playing a key role in unlocking flexibility at the edge of the grid. One of the main barriers to aggregators entering the market is the lack of appropriate models for the price elasticity of flexible demand, which can properly address time dependent uncertainty as well as non-linear and stochastic behavior of end-users in response to time varying prices. In this paper, we develop a probabilistic price elasticity model utilizing quantile regression and B-splines with penalties. The proposed model is tested using data from residential and industrial customers by assuming automation through energy management systems. Additionally, we show an application of the proposed method in quantifying the number of consumers needed to achieve a certain amount of flexibility through a set of simulation studies.

Abstract

The electrification of mining operations is rapidly emerging as a central issue for the resources sector and its efforts to improve reliability and health/safety, and to reduce cost. The reliance on fossil fuel and gas generated electricity is a significant proportion of current mining operational costs and the prevalence of diesel fuel usage is a significant health and safety concern. The use of electric vehicles and machinery combined with partial or stand-alone renewable energy powered microgrids provides a pathway to more efficient, sustainable and safer mining operations, both for underground mining and open-pit mining. Electrification also presents an opportunity to integrate Internet of Things (IoT) technologies such as autonomous vehicles, communications networks and data analysis for safety and higher efficiency. Digitalization and automation is also the solution to reduce operational costs in areas related with concentration and transport. The transition to an electric mining future is complex and will require substantial investment in infrastructure, technologies, and hardware as well as collaboration between the mine operators and service industries, research organisations and regional, State and Federal governments, and newly skilled workforce in Australia. This paper provides an overview of the status of mine electrification and highlights the potential research directions, which aimed to help shape the resource industries transition to an electric and renewables mining future.

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.

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.

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.

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.

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.

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.

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.

• 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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.