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Customers can adjust their own energy usage if the smart grid notifies real-time energy rate which is determined by the current power consumptions. We call these services Demand Response (DR) services. At the Smart Grid environments, consumers can bi-directionally communicate with components of the smart grid through the Advanced Metering Infrastructure (AMI). So the smart gird can provide Demand Response services for reducing energy usage during hours of peak demand. However Demand Response services can be misused if messages between customers and the smart grid are modified by attackers. In this paper, we propose a method to detect the manipulation of message, while the meter reading value is being transferred from Smart Meter to Meter Data Management System (MDMS) through the AMI. It is expected to provide a reliable Demand Response service, and lead to more efficient power usage, through the detecting scheme of this paper. [ABSTRACT FROM PUBLISHER]

This paper investigates the modeling of energy carriers (electricity and gas) systems in the form of “energy hubs” in IRAN. In an energy hub, different energy carriers can interact with each other and an optimization problem can be solved to provide electricity and heat considering constraints on natural gas and electricity networks. Since IRAN has abundant natural gas resources, integrating electricity and natural gas networks within the content of energy hub can be useful in efficiently meeting country's demand for electricity and heat. In this paper, both energy networks are modeled and studied using energy hubs. Each energy hub represents one of the provincial districts in IRAN. Heating loads on different parts of IRAN are estimated and optimal conversion of gas to electricity and heat in each hub is determined. [ABSTRACT FROM PUBLISHER]

Demand management is mechanism to shift the demand of electricity from peak to off-peak to use the available energy as efficiently as possible without requiring additional generation capacity or transmission and distribution infrastructure. Demand response is a special type of demand management which motivates the customers to respond to electricity prices over time. The recently proposed demand response strategies aim at reducing the energy cost of the whole system by shifting energy consumption from peak to off-peak. These methodologies are complex and resource consuming as they try to reach a global optimum for a large group of residences. The paper proposes a simpler demand response strategy to minimize the energy cost in each residence independently using binary particle swarm optimization (BPSO) that schedules the electricity consumption of the shiftable loads in each residence. The paper also proposes a modification of the BPSO algorithm that assists the scheduling algorithm to converge quickly. [ABSTRACT FROM PUBLISHER]

Economic modeling of distributed generation (DG) systems has become an important area of research with the modern push towards greener and more sustainable electricity generation practices as any proposal without a solid business case is bound to flounder with the state of the global economy. This paper assessed the state-of-the-art in DG economic modeling and based on this developed a model to determine the economic suitability of DG projects in Ontario, Canada. This model was applied to the energy profile of the University of Guelph in Guelph, Ontario and it was found that using 2× 5MW biomass combined heat and power (CHP) DGs and selling electricity to the grid will save them $2.56 million annually on energy costs. This paper recommends that further research is done in optimizing between local distribution companies (LDCs) and DG operator economic benefits of DGs, applying risk and uncertainty to economic modeling, analyzing the cost of biomass pellets in Ontario, and doing hour-by-hour modeling of the University of Guelph's energy usage to verify the findings of this paper. [ABSTRACT FROM PUBLISHER]

Recently emerged Support Vector Regression (SVR) model which incorporated temperature and calendar factors as input variables performed well in short term load forecasting. However, lacking of enough economic information in the regression model leads to poor prediction accuracy in months affected by the worldwide economic slowdown effect during years 2008 and 2009. To overcome this problem and improve load forecasting accuracy to better account for this economic slowdown effect, this paper proposes a new SVR approach that further incorporates the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) as additional input variable to SVR model. The proposed SVR approach focuses on predicting island-wide hourly electricity load demand from year 2008 to 2009 in Taiwan. This paper also compares the results with conventional SVR approach, and reveals that the overall load forecasting accuracy, in the best condition, can be improved by 6.6% in average. [ABSTRACT FROM PUBLISHER]

In power system, spinning reserve is one of the important indicators of power system security. Regarding spinning reserve as an important factor, spinning reserve cost has been appropriated highly. This paper proposes a method for decreasing spinning reserve cost using demand respond (DR) program. The effect of DR is focused on the aspect of decrease effect of spinning reserve cost. In order to present the effect of DR, demand-price elasticity matrix is used in this paper. DR program is modeled N-period time-of-use (TOU) program, which is one of the most important economic methods in Demand-side Management (DSM). The changed load is considered two sides of DR effects, which are load curtailment and load shift. Using the changed load after DR, spinning reserve cost is recalculated. Case study is conducted on the RBTS IEEE with six buses. For the TOU program, it is assumed that parameters of time period partition consist of three time periods. Spinning reserve cost is calculated from local marginal price (LMP) and capacity price (CP) of 24 hours a day. [ABSTRACT FROM PUBLISHER]

This paper discusses the basic scheme for establishing advanced metering infrastructure (AMI) by applying a high-voltage electric energy on-site calibration apparatus. The apparatus can work as an on-site working standard for implementing on-site calibration or verification to kinds of meters, electric energy metering cabinet and electric energy device in AMI. Furthermore, this paper provides a technical mean for calculating parameters of power grid, such as transformer loss and wire loss, in accordance with the principle of energy conservation. 10kV high voltage on-site calibration apparatus had been developed successfully and has been running in the power grid, on-site test results can prove that the scheme is feasible. Establishing a complete AMI system is of great significance for building a strong smart grid. [ABSTRACT FROM PUBLISHER]

Because of the Spring Festival effect, it's hard for traditional methods to predict monthly electricity consumption in January and February. This paper presents a specialized subsection-model which can better reflect the situation to solve this problem. In terms of the holiday condition, the model divides the days of the two months into two parts: the usual electricity days and holiday electricity days. In addition, an algorithm is proposed to estimate the parameters of the subsection-model according to data distribution. The data of similar situation are rare because the date of Spring Festival is different every year, the algorithm overcomes this difficulty. Experiments conducted on real dataset prove the proposed model is effective and accurate. [ABSTRACT FROM PUBLISHER]

Distributed generation is the process of generating electricity from small energy sources and connecting it to the distribution network or on the customer's side of the meter. The placement of distributed generation and the size of the distribution generation is important. Determining the optimal location and sizing of a Distributed Generation (DG) Unit is being presented in this paper. The radial distributed network has been considered. A robust stochastic optimization technique based on the movement and intelligence of swarms called Particle Swarm Optimization has been implemented to obtain optimal solution of the DG placement problem. Reduction of line losses in the radial distribution network is the main objective of this paper. [ABSTRACT FROM PUBLISHER]

This paper establishes a robust model to adjust the hourly load level in response to hourly electricity price of a given consumer .The objective of the model is to maximize the utility of the consumer subject to a minimum daily energy supply level, maximum and minimum hourly demand levels, and ramping limits on such demand levels. Unknown price is forecasted through neural network with a confidence interval. A simple bidirectional communication device between the power supplier and the consumer enables the achievement of the proposed model. Numerical simulations are provided. [ABSTRACT FROM PUBLISHER]

At present, electrical energy becomes more and more important, and the shortage of electrical energy becomes serious. In order to develop power efficiency and alleviate the problem, people have planned the methods of load identification. This paper is on the basis of many different kinds of methods, and used K-means classification and fuzzy algorithm. This kind of method also needs to store the templates in database in advanced, and compare the features of samples which are extracted from the experiment with templates. Then it utilizes K-means classification to classify and uses fuzzy algorithm to calculate the closeness degree of every cluster. The industrial environment is very complex. E.g. the number of load types, states, some interference factors etc. This method we proposed can handle large amount of data and has another key of advantage. It is the capability to identify major industrial loads accurately. The results are presented in this paper. All in all, industrial load identification needs to be consummated. [ABSTRACT FROM PUBLISHER]

In order to take advantage of wind resource which is not rich in Shaanxi northern region and the annual average wind velocity is a little more than 4m/s, this paper focused on researching the comparison of theoretical potential application of vertical axis wind turbines (VAWT) and horizontal axis wind turbines (HAWT) in Northern Shaanxi. Wind velocity distributions were calculated by the Rayleigh distribution method based on local meteorological data in Yulin and Yan'an where the most common wind velocity is 3 or 4m/s. Then this paper based on the working characteristics of 600W permanent magnet generator, simulated the working performances of VAWT and compared with traditional HAWT. The results showed that more than 70 percent wind resource concentrated at wind speed ranged is from 3m/s to 8m/s, working hours of VAWT were more than 2000 hours in a year, and electricity generated of VAWT was over 50 percent more than traditional HAWT. Thus, although the aerodynamic performances of the vertical axis wind turbine are less efficient, we can conclude that VAWT have higher torque, less starting wind speed which will be more effective to match wind resource distribution of North Shaanxi and have more potential applications than HAWT. [ABSTRACT FROM PUBLISHER]

Widespread adoption of onsite distributed generation providing households with their electricity supply could make them very significant actors in terms of their aggregated impact on the wider electric power system. This paper proposes an approach which brings community engagement to the case of distributed energy resource based electricity supply for urban residential buildings. Considering a future case for Stockholm, a community ownership and service-oriented setup is proposed which fosters the creation of a market for both traditional and new stakeholders in the electric power system while ensuring the integrity of control by individual apartments. Salient features include a hypothetical pricing model which encourages local trade of electricity between apartments guided by individual apartment behavior and decision making through bid strategies. A set of research themes devoted to understanding the trends in effect of changes in the integral concept components have been identified through simulations and subjected to initial analysis. [ABSTRACT FROM PUBLISHER]

This paper presents a thorough analysis of 15-minute residential meter data sets to identify possible value propositions of smart meter measurements. Meter measurements of 50 houses were used to derive a few key data signatures for several target applications such as identifying demand response potentials, detecting abnormal load behaviors, and fault diagnosis. Results showed that for different applications, the communication needs from meters to control centers, data storage capabilities, and the complexity of data processing intelligence varies significantly. Therefore, it is important to build a dynamic data signature database and optimize the distribution of data processing capability between local devices and control centers to avoid communication congestion and to identify problems early. This paper also demonstrates that highresolution smart meter data can make distribution power grids more economical, reliable, and resilient. [ABSTRACT FROM PUBLISHER]

This paper deals with decision making for new photovoltaic power plant establishment in Iran's electrical network. Iran Electricity network consists of 16 Regional Electricity Companies. In order to find an appropriate place for Photovoltaic power plant, different aspects of the problem have been evaluated and different relative weights are assigned to them. Finally, problem is formulated and solved using Expert Choice software by AHP method. [ABSTRACT FROM PUBLISHER]

Smart grids are integrated communications and power system infrastructures to improve the efficiency, reliability and safety of power deliveries and its usages. In this paper, Time of Use (TOU) programs in smart grids is studied. In this regard, Real Time Pricing method (RTP) has been developed using multi objective load models. Also, the effects of smart grids on demand curve have been simulated by different scenarios. Also, elasticity criterion in this method is optimized, and after that the best elasticity value is achieved. Also, Iran's national grid has been selected to analyze the effects of real time pricing in smart grid environments on load curve. Meanwhile, the results have been compared with TOU programs for better presentation of proposed method and finally excellent performance of the presented technique will be shown. [ABSTRACT FROM PUBLISHER]

This paper aims to present a framework for understanding, characterizing, and assessing the economic value possible from integration of Electric Vehicles into the Australian (NEM) electricity industry. The broader goal of EV integration is to maximize economic value through the enhancement of short run (allocative) and long run (dynamic) economic efficiency. A framework for decision making in a restructured electricity industry is used to assess the current ‘state of play’ with respect to Electric Vehicle integration into current NEM arrangements. In addition, a set of value categories with respect to the economic value of Photovoltaic system integration into the NEM is adapted and applied Electric Vehicle integration. The approach presented in this paper is intended to establish a framework which will inform later detailed studies quantifying the economic value of various aspects of Electric Vehicle integration in the Australian NEM. [ABSTRACT FROM PUBLISHER]

The anticipation of a large penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) into the market brings up many new technical problems that need to be addressed. In the near future, a large number of PHEVs/PEVs connected to power grids will add a large-scale energy load, as well as add substantial energy resources that can be utilized. Vehicle-to-Grid (V2G) technology is a most promising opportunity in PHEV/PEV adoption. In this paper, the authors propose an intelligent method for optimally managing a large number of PHEVs/PEVs (e.g., 3,000) charging/discharging at a municipal parking deck. The authors used the Estimation of Distribution Algorithm (EDA) to determine the optimal charging/discharging times and patterns over a period of 24 hours. A mathematical framework for the objective function (i.e., maximizing the overall profit on a vehicle fleet base) is also given in detail. The authors characterized the performance of EDA-based energy management using a Matlab simulation, and compared it with other optimization techniques. [ABSTRACT FROM PUBLISHER]

Around the world there are many rural areas need access to electricity, extend the transmission line is one option. However, the long distance between the nearest main grid and the rural system as well as the costs of transmission line expansion rapidly increasing makes grid extension difficult, costly and economically unviable. Therefore, using microgrid technology with renewable energy options to meet electricity demand in remote locations becomes more attractive. In this paper the break-even distance which makes electricity from microgrid cost effective over the one form main grid is calculated. Moreover, the Net Present Costs (NPC) of both providing electricity through the microgrid and the main grid are calculated and compared. Five different cases including a diesel-only, a fully renewable-based, a diesel-renewable mixed, a solar-only, and a wind-only microgrid configurations are designed, to compare and evaluate their economics with the main grid. The well known energy modeling software for hybrid renewable energy systems, HOMER is used in the studies reported in this paper. [ABSTRACT FROM PUBLISHER]

This paper aims at designing a CO2 emissions management program. Within a framework of the free market economy, we present a system model for managing the CO2 emissions in an industry while affirming the profit seeking competition among individual firms. The model provides a system-wide rate of charge for CO2 emissions instead of strictly regulating production activities. [ABSTRACT FROM PUBLISHER]

The load management of utility feeders for rural irrigation in India is a tough task due to various uncertainties of demand and pilferage of power. Presently most of the Indian utilities manage the irrigation load either by switching off 11 KV feeders by rotation or single phasing of 11 KV feeders by rotation. Because of these methods power supply gets interrupted even for domestic, commercial and industrial loads. Further many consumers still run pump sets using single phase to three phase converters therefore overloading the two phases of the distribution transformer, which may lead to winding failure of distribution transformers. Therefore an urgent need is felt to switch over to a new scheme which shall overcome the drawbacks of the present practices and results in energy as well as monetary savings. This electronic document is a ‘live’ template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document. This paper presents the optimal solution for load management of irrigation pump load by considering a real time load of one of the utility region in Punjab state of India. With this solution the power supply shall not be disturbed for domestic and commercial load along with full utilization for irrigation pump sets also. The power consumption by the pumps will also increase every year due to lowering ground water level and multiple cropping in a year. Therefore the losses should be reduced with increased demand of power in irrigation sector to ensure reliable power supply. Also the load should be properly distributed to give a good load factor. This will result in savings of max demand, which can be utilized at some other place. Further, with this reduced load the line losses will also reduce. Some additional measures for energy management of the irrigation pump loads on the transformer are proposed in this paper as introduction of LT capacitors and use of amorphous core transformers. The introduction of LT capacitor panels results in power factor correction along with additional savings in the form of line losses. Also old transformers can be changed with amorphous metal transformers, in phases, to realize savings in no -load losses. The paper presents cost-benefit analysis for the real time load of one of the utility region in Punjab state of India which shows payback period of less than one year for these options. [ABSTRACT FROM PUBLISHER]

Demand response is a key element of the smart grid technologies. This is a particularly interesting problem with the use of dynamic energy pricing schemes which incentivize electricity consumers to consume electricity more prudently in order to minimize their electric bill. On the other hand optimizing the number and production time of power generation facilities is a key challenge. In this paper, three models are presented for consumers, utility companies, and a third-part arbiter to optimize the cost to the parties individually and in combination. Our models have high quality and exhibit superior performance, by realistic consideration of non-cooperative energy buyers and sellers and getting real-time feedback from their interactions. Simulation results show that the energy consumption distribution becomes very stable during the day utilizing our models, while consumers and utility companies pay lower cost. [ABSTRACT FROM PUBLISHER]

The paper deals with innovative idea that is to move beyond the current concept of smart grid, proposing a new architecture, called Sustainable Energy Microsystem (SEM). The proposed design includes energy sub-systems currently independent: high efficiency buildings, sustainable mobility systems, dispersed generation from renewables and Combined Heat and Power units. In the paper, the authors show the main figures of the SEM and report the first results about the necessary preliminary energy analysis. [ABSTRACT FROM PUBLISHER]

John Holdren states there are three responses to climate change: mitigation, adaptation, and suffering. Research abounds in framing mitigation and adaptation policies. Furthermore, many suffering criteria exist in the health care literature as a physiological and psychological component to patients' care, but not in the technological aspects to hospitals. In this paper, the socio-technological challenges of renewable electricity in Uganda are explored to frame the reality of unreliable electricity within a suffering criteria. For this paper, we define suffering criteria as avoidable damage (unmet power load) which is highly dependent on balancing variability and uncertainty. For example, there is a measurable variable solar radiance profile. On any specific day, dispatch microgrid control algorithms consider the measured variability as an uncertainty, but here we define uncertainty as adding random noise to the measured variability using Monte-Carlo simulations. By doing this, we have a renewable energy system defined within a suffering criteria that is clearly illustrated within bounds (no variability, measured variability, and simulated additional variability from uncertainty). Unmet load data is generated using HOMER Energy. This study can help further understand variability and uncertainty in renewable energy sources in hybrid microgrids as it is framed within a suffering criteria — avoidable damage on health care due to unmet power load. It is vitally important due to renewable energy system trade-offs between overdesign (levelized cost of electricity > $1/kWh) and underdesign (capacity shortage > 50%). It leads to motivations for redundancy in microgrids similar to redundancy in large-scale centralized grids. [ABSTRACT FROM PUBLISHER]

The dynamics of commodity prices has become a major field of analysis in the last 20 years. Standard econometric procedures to describe the behavior of prices have not been able to provide accurate description of the real dynamics. In this paper we apply filter banks to predict prices of specific energy commodities: crude oil, natural gas and electricity, which play a crucial role in the international economic and financial context. Given the high volatility of energy commodity prices, an accurate short term prediction allows to set adequate risk management strategies for producers, retailers and consumers. Filter banks for subband decompositions of the sequences to be predicted are proposed in the paper, allowing the implementation of a parallel computing system to get faster and more accurate implementation. The prediction system is based on a neural model trained on each subband according to specific training and prediction techniques. [ABSTRACT FROM PUBLISHER]

In this paper, we present our preliminary implementation of a market-based agent-mediated resource control framework for dynamic electric power allocation. Although market-based approach is promising in the researches for effective resource allocation and control on smart girds, its effective and flexible implementation is crucial. In this paper, we present a prototyping environment to utilize both market-based approaches (e.g., auctions) and multiagent-based approaches (e.g., ACL-based complex communications) for controlling middle-scale smart grids. We discuss about a possible performance based on our preliminary implementations and further issues to be resolved. [ABSTRACT FROM PUBLISHER]

Electricity consumption forecast is very important for both suppliers and large consumers. The electricity consumption of a large enterprise is different from the regional grid, especially for an energy intensive enterprise (EIE). This paper investigates the daily electricity consumption forecast of an EIE by the operating condition. By observation, the electricity consumption is related to maintenance duration and production quantity. And through verification, the relationship is linear. Therefore, the production plan and maintenance schedule can be considered as the input of the forecast model. After the preliminary selection of the feature set, the Nonnegative Least Squares (NNLS) method is applied to build a linear regression model with nonnegative coefficients. In addition to NNLS, a new feature selection method based on correlation analysis and greedy search is applied to select the relevant input features on the available items of the production and maintenance schedule, which has greatly improved the efficiency of feature selection, and decreased the influence of multicollinearity and local optimum. By using the real data from a typical modern large steel corporation, numerical test results show that results obtained by the NNLS are rational, and the improvement of forecast results are obvious in several evaluation criteria. [ABSTRACT FROM PUBLISHER]

To improve the energy utilization efficiency of power systems and solve the bottleneck problem of wind power development, the combined operation mode of wind power and pumped storage power could be adopted where there were some water resources. Based on the analysis the impact of large-scale wind power farm on the grid and the necessity of combined operation, considering the current price mechanism in China, this paper applies the incentive compatibility principle to propose the two-part electricity price for pumped-storage power station mode based on correctly guide the investment of power station and promoting the safe and economical operation of power system. It is able to encourage the combined mode. In the end, examples results show the two-part electricity price for pumped-storage power station can effectively encourage the combined operation mode and promote the wind power industry healthy development. [ABSTRACT FROM PUBLISHER]

Based on the analysis of evaluating framework of power grid planning in industrial cluster districts, this paper put forward the evaluating indices including power supply adequacy and electricity consumption correlation, establishes a model combined with BCG(Boston Consulting Group Growth-Share) model and the present situation of power supply, which is to be applied to the evaluation of power grid programming, and finally, taking Henan province for an example, an practical analysis is made to verify the applicability and rationality of the model which is surely of great practical significance to Henan power network planning optimization. [ABSTRACT FROM PUBLISHER]

This paper used panel regression and GARCH analysis to investigate changes in volatility of total returns of European energy companies due to energy sector liberalisation events. The paper constructed an index of 24 energy companies and calculated a 22 days rolling standard deviation of daily total returns over a 21 year period. Eight liberalisation events were identified. An unbalanced random-effects panel regression was used to analyse liberalisation's impact on the standard deviation. A GARCH (Generalised Autoregressive Conditional Heterosce-dasticity) analysis was used on the constructed index to measure volatility clustering and conditional variance. The panel regression showed that overall volatility has increased, however some liberalisation events decreased volatility. The GARCH analysis demonstrated that returns increased over the time period and found high autocorrelation, but no conditional variance using a 1 day lag. Our analyses show that both risk and return have increased during liberalisation with the largest increases in volatility and return occurring when businesses and households could freely choose suppliers. [ABSTRACT FROM PUBLISHER]

In this paper, we analyse the impact of the introduction of more sophisticated types of bids on the wholesale day ahead market for electricity. The Belgian market is taken as a case study with available data used for the period 2007–2010. Sophisticated orders allow physical assets to be allocated in the most efficient way, even if prices deviate significantly from expectations. In this paper we analyse the quantitative impact on spot prices, both in terms of level and variability, if smart bids had been introduced. The main focus lies on tailor made sophisticated orders for pumped storage. The analyses are based on market resiliency data of Belpex, the Belgian Power exchange, and real historical data on available pumped storage plants. The findings could tip the balance to deepening the market coupling along with the widening to other geographical areas. Our results indicate that straight-forward solutions can improve the optimal allocation of scarce resources in the day ahead electricity market. [ABSTRACT FROM PUBLISHER]

This paper analyses the application of levelized costs criterion to energy markets when there is a significant share of production from intermittent technologies in the power generation system. Under such scenario, the share of dispatchable technologies is often reduced, and producers may face difficulties to recover their production costs trough market. [ABSTRACT FROM PUBLISHER]

This paper analyses in two scenarios the impacts that climate policies, especially the support of renewable energies and the price of carbon, will have on the European electricity system. A central focus is the impact that electricity generated from renewable energy sources (RES-E) on other system components such as conventional power generation technologies, transmission grids and electricity storage facilities. For the analysis in this paper detailed feed-in profiles for each hour of the year are derived for fluctuating generation from wind and solar power based on actual meteorological data from weather stations and satellites. The model PowerACE-Europe simulates all year simultaneously in hourly resolution, covering each country of the EU-27 as well as Norway and Switzerland. The model calculates the least-cost solution for meeting demand in every hour by simulating investment in power generation technologies, grids and storages by solving a large linear problem through interior-point optimization. [ABSTRACT FROM PUBLISHER]

Electricity transmission infrastructure has a fundamental role in the process of integrating national electricity markets in Europe to a single market. Transmission expansion takes place within institutional frameworks that sets the conditions for expansions. So far, most of the transmission business has been organised under the centralised and regulated transmission system operators (TSOs). This is motivated for reliability and grid access reasons. However, a strictly centralised approach to transmission expansion may not be capable of meeting the needs of the market. Central planners cannot incorporate all relevant information and knowledge about the market and its unexploited opportunities. Market participants have context specific knowledge that generates entrepreneurial actions and innovations. This is an essential driving force of markets and it needs to be included in transmission development. The strong interdependency aspect of grid expansions does require some form of common governance. However, for the electricity market to develop, it is also likely that it requires the decentralised actions of entrepreneurial market actors. Can both of these requirements be met? This paper elaborates on several key determinants for developing a market friendly infrastructure without losing control. [ABSTRACT FROM PUBLISHER]

This paper addresses the role of public policy in influencing technological innovation and diffusion in the energy-intensive process industries (e.g., pulp and paper, iron and steel etc.). We highlight in particular technologies that either reduce the industry's energy use or imply lower process emissions of carbon dioxide. This research scope is motivated due both to its societal relevance and the fact that it raises a number of important scientific challenges and controversies. Specifically, the paper discusses the role of two different theoretical perspectives for orienting the analysis of issues in technology policy: the neoclassical and the evolutionary approaches. The overall objectives of the paper are to: (a) provide a conceptual comparison of these theoretical approaches and their implications for the role and the design of policy intervention; and (b) discuss some relevant implications for spurring innovation processes by examining the experiences of two existing policies and one future technological challenge (CCS) in the Swedish process industries. [ABSTRACT FROM PUBLISHER]

The paper reports on the potential challenges and opportunities of an electric power trans-Canadian grid (TCG). The report, which is the first stage of a broad research project on the TCG, identifies and examines the main issues from a qualitative view point and sets the stage for further quantitative analysis. The paper evaluates the competitive advantage of a TCG to meet established planning and operation norms such as cost and reliability as well as more topical criteria related to emissions control as a result of concerns over climate change. The proposed TCG would interconnect the provincial grids more tightly through one or a combination of possible topologies elaborated upon in the paper. The possible advantages of such a project are discussed in detail as are the expected barriers. [ABSTRACT FROM PUBLISHER]

Distribution system operators (DSOs) are facing new challenges when more distributed generation (DG) is connected to the network. In this new operating environment the DSO has to be able to plan an efficient network topology, which consists of reinforcement and extensions. In addition, the DSO has to finance the investment from tariffs. The methods to solve network planning problems are reviewed in this paper. The studied network planning problem is the case when the DSO has no influence on the location of DG due to the unbundling between DSOs and production. Furthermore, the regulation for how the DSOs are allowed to design the tariffs in systems with DG vary between countries, a comparison of how the DSOs design their tariffs under different regulations is presented. This paper ends with a case study on methods that Swedish DSOs use to plan the networks when considering the uncertainties caused by wind power and the regulation impact on distribution network planning and network tariffs in Sweden. [ABSTRACT FROM PUBLISHER]

Since 1987, many countries have decided on their electricity market liberalization, increasing their degree of maturity, efficiency and, finally, liquidity. This paper aims at presenting the main concepts of liquidity and its importance for the satisfactory functioning of electricity markets. Likewise, it assesses the Brazilian electricity market liquidity by presenting the perspective of the electricity contracts traded in Brazil, focusing on the free market, exploring further the contribution of various classes of agents in the liquidity of these products. For instance, it portrays the current liquidity scenario by presenting the churn rates of conventional and special energy short and long term contracts and the liquidity relative rates which are an innovative adaptation from the stock market to calculate the liquidity of the referred products. Finally, this paper lists some actions that could be implemented by the Brazilian electricity market in order to improve its liquidity level. [ABSTRACT FROM PUBLISHER]

This paper examines a series of scenarios achieving progressively very high levels of renewable energy sources (RES) share in the European electricity generation mix in the context of a roadmap to a low carbon economy in Europe quantified using the PRIMES model. Such high RES penetration levels practically require particularly high deployment of wind and solar, which are variable, else intermittent, sources of power. Implications that this inconvenient characteristic could have on the development of the European electricity system are investigated in this paper. Two moderating technologies are modeled, simulated and analyzed in terms of their ability to deal with the issue of intermittency: a) storage of energy in the form of hydrogen, and b) new DC transmission grid investments. A set of results are provided, illustrating the effect of each of these technologies. [ABSTRACT FROM PUBLISHER]

Although the Portuguese electricity system liberalization process started almost 20 years ago, 83% of electricity generated in 2010 still benefited from a State guaranteed price, independent of market behavior. This applies not only to producers using renewable energy sources and cogeneration under feed-in tariffs, but also to all conventional power plants that undersigned a power purchase agreement in the 1990s. Because no comprehensive public information about State guaranteed prices is available, research was first carried out to unveil generation legacy contracts' volumes and then costs and their impact on consumers. A producers' database was created by the authors which enables an accurate assessment of the volumes of electricity with and without State guaranteed prices. In a previous paper the authors have validated the data set and the volume computation methodology for the period 2000–2010 and provided an estimate of those volumes in the period 2011–2030. This paper estimates average prices per producer type and assesses generation legacy contract costs in the period 2011–2030. Moreover, the paper computes the impact of generation legacy costs upon end-user average electricity prices and generation costs for several scenarios of market prices and demand. [ABSTRACT FROM PUBLISHER]

This paper presents a Markov chain approach for developing low-voltage (LV) residential load models for use in the analysis of future smart grids. The motivation is to obtain a better understanding of load use and user behaviour at the LV level which will allow future electricity network performance to be improved by understanding the impact of energy demand transformations through the application of demand-side management (DSM) strategies at the LV side. The developed load models highlight the variations between individual and aggregate load model representation and show that the composition of LV residential users should be carefully considered when analysing LV networks. [ABSTRACT FROM PUBLISHER]

Electric vehicles (EVs) are a key technology to reduce dependency on oil imports as well as to diminish environmental effects of individual transportation. Especially in megacities like Singapore where travel distances are moderate, this new mode of transportation is often discussed as a future option. This paper investigates possible effects of large scale EV integration on the power supply system. A unit commitment model combined with an integrated approach for smart charging is used. The mixed-integer linear programming (MILP) formulated unit commitment algorithm cooptimizes energy, regulation, and spinning reserve power. The effects of different charging strategies on the power plant scheduling are analyzed. The power system infrastructure is kept at status quo in a baseline scenario and extended to future scenarios with intermittent photovoltaics (PV) power. Effects on power plants scheduling are evaluated by measuring resulting variable cost of electricity as well as CO2-emissions. Moreover, effects of EVs providing regulation and spinning reserve by controllable charging are investigated. [ABSTRACT FROM PUBLISHER]

Electric Vehicles (EVs) have gained large interest in the energy sector as a carrier to support clean transportation and green electricity. The potential to use battery storages of electric vehicles as a sink for excess electricity that may result from large integration of wind power, especially in countries like Denmark, is widely discussed and promoted. However, the wide-spread adoption of EVs requires the provision of intelligent grid and EV charging infrastructure. To analyse and understand the amount of EVs that could be integrated in the local distribution grids, within its existing capabilities, is absolutely essential for the system operators to plan and implement the levels of grid reinforcement and intelligence required. This paper investigates the local grid limitations to accommodate large amount of EVs of sizable power ratings in residential areas. The case study applied in this paper uses a detailed secondary distribution grid model in Denmark. Various EV integration scenarios are analysed in this work to understand the network operational flexibility and ruggedness. The simulation results show that level of EV integration varies with the strength of different feeders in the studied network. Simple grid reinforcement measures like adding new feeders in the existing network improves EV penetration levels. [ABSTRACT FROM PUBLISHER]

For a large-scale introduction of electric vehicles (EVs) intelligent charging strategies are needed to adjust their energy demand to the production of renewable energy and the current load in the distribution grids. This paper presents an approach that puts the intelligence needed into the EVs, where most of the crucial information arise, thus minimizing the need of communication throughout the grid. The EVs shall be enabled to decide on a charging strategy using an optimization algorithm which creates a charging schedule based on the charging point's and the EV's capabilities, user input, battery degradation and a variable tariff for electricity. This algorithm has been implemented in a control device called the mobile Smart Meter (mSM), which is embedded in the EV, enabling it to handle the necessary communication and to create and monitor an optimized charging strategy. The results of a small-scale field trial shown in this paper delivered the proof of concept of the developed system. [ABSTRACT FROM PUBLISHER]

The operation of power systems in a Smart Grid (SG) context brings new opportunities to consumers as active players, in order to fully reach the SG advantages. In this context, concepts as smart homes or smart buildings are promising approaches to perform the optimization of the consumption, while reducing the electricity costs. This paper proposes an intelligent methodology to support the consumption optimization of an industrial consumer, which has a Combined Heat and Power (CHP) facility. A SCADA (Supervisory Control and Data Acquisition) system developed by the authors is used to support the implementation of the proposed methodology. An optimization algorithm implemented in the system in order to perform the determination of the optimal consumption and CHP levels in each instant, according to the Demand Response (DR) opportunities. The paper includes a case study with several scenarios of consumption and heat demand in the context of a DR event which specifies a maximum demand level for the consumer. [ABSTRACT FROM PUBLISHER]

With the advent of plug-in electric vehicles (EV), it becomes increasingly important to study the impact of charge scheduling of a large number of EVs on the day-ahead electricity market price. In this paper, two scheduling models are proposed-joint scheduling model and aggregator scheduling model. They are used to study the effects of scheduling of EV charging on the day-ahead market price of electricity at various penetration levels of the EVs. Results from studies on an IEEE 30-bus test system show that, at lower penetration of EVs, a simple scheduling method using fixed-period charging during low demand periods can be used, without a large increase in market price. But at higher penetration levels, the opposite holds true, requiring advanced scheduling methods such as proposed in this paper. Between the two, joint scheduling model results in a lower increase in electricity price and is further tested on a simplified Nordic day-ahead market model. An overall conclusion is that the Nordic day-ahead market can accommodate large penetration of EVs without a significant increase in market price. [ABSTRACT FROM PUBLISHER]

In competitive electricity markets with deep concerns at the efficiency level, demand response programs gain considerable significance. In the same way, distributed generation has gained increasing importance in the operation and planning of power systems. Grid operators and utilities are taking new initiatives, recognizing the value of demand response and of distributed generation for grid reliability and for the enhancement of organized spot markets' efficiency. Grid operators and utilities become able to act in both energy and reserve components of electricity markets. This paper proposes a methodology for a joint dispatch of demand response and distributed generation to provide energy and reserve by a virtual power player that operates a distribution network. The proposed method has been computationally implemented and its application is illustrated in this paper using a 32 bus distribution network with 32 medium voltage consumers. [ABSTRACT FROM PUBLISHER]

The interest in electric vehicles (EVs) is rapidly increasing, and the upward trend seems to continue also in the future. As a result of the increasing energy consumption, the grid infrastructure has to be developed further, and moreover, it is necessary to analyze the network effects of the electric vehicle charging. In this paper, the challenge is approached by an actual case area in Finland and simulated EV traffic. The data used in the simulation are collected by measuring the traffic flow on the road leading to the case area. The objective of the paper is to demonstrate how the grid effects of large-scale electrification of transportation can be addressed, and what data are required to assess the additional charging load in a feasible manner. The data are processed by applying the Monte-Carlo method, and also a sensitivity analysis is performed. [ABSTRACT FROM PUBLISHER]

In this paper, short-term electricity price forecasting considering residual electricity demand is investigated. Residual, or net, demand is determined by subtracting any unpredictable generation from the system load. Focusing on wind energy as the main hard-to-predict source of electricity, we first examine the dependency of short-term electricity prices and wind power using data association mining algorithms. Second, we investigate the impact of including net demand in short-term electricity price forecasting, and we propose a new electricity price forecasting model. Data from the Alberta and the Nordic electricity markets are used to conduct studies and evaluate the forecasting results. [ABSTRACT FROM PUBLISHER]

This paper reports on a food converting business located in South africa with thirteen manufacturing sites, producing bread for the local market. The paper outlines the methodology used to establish a carbon footprint and Energy intensities for the baking of bread. Further, the energy mix for the factories was established and a review of this mix is discussed. It is proposed that an understanding of the current state could determine a potential improvement gap if benchmarked against other producers. This is seen as a first step to creating a platform for energy efficiency improvement and further research within this sector. [ABSTRACT FROM PUBLISHER]