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We investigate the potential for aggregations of residential thermostatically controlled loads (TCLs), such as air conditioners, to arbitrage intraday wholesale electricity market prices via non-disruptive load control. We present two arbitrage approaches: 1) a benchmark that gives us an optimal policy but requires local computation or real-time communication and 2) an alternative based on a thermal energy storage model, which relies on less computation/communication infrastructure, but is suboptimal. We find that the alternative approach achieves around 60%–80% of the optimal wholesale energy cost savings. We use this approach to compute practical upper bounds for savings via arbitrage with air conditioners in California's intraday energy market. We investigate six sites over four years and find that the savings range from $2–$37 per TCL per year, and depend upon outdoor temperature statistics and price volatility. [ABSTRACT FROM PUBLISHER]

This paper aims to evaluate and improve the usefulness of publicly available electricity market prices for real-time optimal dispatching (RTOD) of a privately owned energy storage system (ESS) in a competitive electricity market. The RTOD algorithm seeks to maximize the revenue by exploiting arbitrage opportunities available due to the inter-temporal variation of electricity prices in the day-ahead market. The pre-dispatch prices, issued by the Ontario independent electricity system operator, and the corresponding ex-post hourly Ontario energy prices are employed as the forecast and the actual prices. A compressed-air ESS is sized and employed for evaluations due to its lower capital expenditure and its ability to be positively influenced by the availability of waste heat. First, the conventional RTOD algorithm is developed by formulating a mixed integer linear programming problem. It is demonstrated that the forecast inaccuracy of publicly available market prices significantly reduces the ESS revenue. Then, a new adaptive algorithm is proposed and evaluated which adapts the objective function of the optimization problem online based on historical market prices available before real-time. The outcomes reveal that the proposed adaptive RTOD can significantly increase the ESS revenue compared to the conventional algorithm as well as the back-casting method proposed in prior studies. [ABSTRACT FROM PUBLISHER]

In this paper we study the spatial and temporal distribution of the wind and hydro resources, together with electricity demand and prices, with the objective to gain insights on the trade-offs of developing wind power in different parts of the country. It is particularly important to understand the impacts of a high penetration level of wind power on an electricity market that is already highly influenced by seasonal hydro power availability. The correlation between the seasonal patterns of wind, hydro, demand and prices has received little attention in the literature. In our approach, we separate daily deviations from the expected seasonal value, and show that for some locations it is the anomalies that have a more significant correlation with electricity prices than the seasonal patterns themselves. This indicates that the correlation results for anomalies from different wind sites can give valuable information on wind resource availability in critical periods of the year, such as dry seasons, and thereby enables identifying sites that can most optimally balance price volatility during these periods while potentially also maximising profits to investors. Even though the numerical results pertain to New Zealand, our methodology is applicable to any electricity system that utilises wind and hydro resources such as Chile, Colombia and several North American and Northern European jurisdictions. [ABSTRACT FROM AUTHOR]

Many empirical exercises estimating demand functions, whether in energy economics or other fields, are concerned with estimating dynamic effects of price and income changes over time. This paper first reviews a number of commonly used dynamic demand specifications to highlight the implausible a priori restrictions that they place on short and long-run elasticities. Such problems are easily avoided by adopting a general-to-specific modeling methodology. Second, it discusses functional forms and estimation issues for getting point estimates and associated standard errors for both short and long-run elasticities--key information that is missing from many published studies. Third, our proposed approach is illustrated using a dataset on Minnesota residential electricity demand. [ABSTRACT FROM AUTHOR]

This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines auto-regressive integrated moving average (ARIMA) and neural network (NN) models in a cascaded structure and uses explanatory variables. A two step procedure is applied. In the first step, the selected explanatory variables are predicted. In the second one, the energy prices are forecasted by using the explanatory variables prediction. Further, the proposed model considers a multi-step ahead price prediction (12 weeks-ahead) and is applied to Brazilian market, which adopts a cost-based centralized dispatch with unique characteristics of price behavior. The results show good ability to predict spikes and satisfactory accuracy according to error measures and tail loss test when compared with traditional techniques. Thus, the model can be an attractive tool to mitigate risks in purchasing power. [ABSTRACT FROM AUTHOR]

Rooftop photovoltaic (PV) systems can be readily deployed on industrial halls with a relatively large rooftop area. The feed-in tariff above the base price of electricity is offered in many countries to subsidize the high initial investment of PV systems. To fully capitalize the benefit of the feed-in tariff, the investigation of the actual performance of PV systems under case-specific conditions is very important. With building energy simulation, this paper explores the cost–benefit of implementing PV systems of different capacities for a few different cases of industrial halls. The impact of various economic parameters is also investigated. [ABSTRACT FROM PUBLISHER]

Currently, there are many techniques available for short-term electricity market clearing price (MCP) forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. Mid-term electricity MCP forecasting has become essential for resources reallocation, maintenance scheduling, bilateral contracting, budgeting and planning purposes. A hybrid mid-term electricity MCP forecasting model combining both support vector machine (SVM) and auto-regressive moving average with external input (ARMAX) modules is presented in this paper. The proposed hybrid model showed improved forecasting accuracy compared to forecasting models using a single SVM, a single least squares support vector machine (LSSVM) and hybrid LSSVM-ARMAX. PJM interconnection data have been utilized to illustrate the proposed model with numerical examples. [ABSTRACT FROM AUTHOR]

Thanks to its very simple recursive computing scheme, exponential smoothing has become a popular technique to forecast time series. In this work, we show the advantages of its multivariate version and present some properties of the model, which allows us to perform a dynamic factor analysis. This analysis leads to a simple methodology to reduce the number of parameters (useful when the dimension of observations is large) via a linear transformation that decomposes the multivariate process into independent univariate exponential smoothing processes, characterized by a single smoothing parameter that goes from zero (white-noise process) to one (random walk process). A computer implementation of the expectation-maximization (EM) algorithm has been built for the maximum likelihood estimation of the models. The practicality of the method is demonstrated by its application to hourly electricity price predictions in some day-ahead markets, such as Omel, Powernext, and Nord Pool markets, whose forecasts are given as examples. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

Numerous studies assume that the price elasticity of electricity demand remains constant through the years. This, in turn, means that these studies assume that industrial consumers react in the same way to price fluctuations regardless of the actual price level. This paper proposes that the price elasticity of industrial electricity demand varies over time. The Kalman filter methodology is employed in an effort to provide policy-makers with more information on the behaviour of the industrial sector with regards to electricity price changes, focusing on the period 1970 to 2007. Other factors affecting electricity consumption, such as real output and employment, are also captured. The findings of this paper show that price sensitivity has changed since the 1970s. It has decreased in absolute values from -1 in 1980 to -0.953 in 1990 and then stabilised at approximately -0.95 which indicates that the industrial sector has experienced an inelastic demand. In other words, the behaviour of industrial consumers did not vary significantly during the 2000s. In the long run and as the prices increase, probably reaching the levels of the 1970s or even before, the industrial sector's behaviour might change and the elasticity might end up at levels higher than one (elastic). [ABSTRACT FROM AUTHOR]

This paper shows how the effect of fuel prices varies with the level of electricity demand. It analyzes the relationship between daily prices of electricity, natural gas and carbon emission allowances with a semiparametric varying smooth coefficient cointegration model. Different electricity generation technologies have distinct fuel price dependencies, which allows estimating the structure of the power plant portfolio by exploiting market prices. The semiparametric model indicates a technology switch from coal to gas at roughly 85% of maximum demand. This model is used to analyze the market impact of the nuclear moratorium by the German Government in March 2011. Futures prices of electricity, natural gas and emission allowances are used to show that the market efficiently accounts for the suspended capacity and correctly expects that several nuclear plants will not be switched on after the moratorium. [ABSTRACT FROM AUTHOR]

Heat pump water heaters have been introduced to improve energy efficiency of water heating in buildings. Although much research has been conducted to make up for weak points of heat pumps which occur when their purpose is converted from air-conditioning into water heating, there remain some points to be improved to perform as stably as conventional water heaters. Recently, the hybrid water heating system, which is the combination of a heat pump and a traditional gas fired water heater, is proposed as an alternative water heater to perform more efficient water heating stably. In this paper, the hybrid system is designed for general residential houses in Korea, which have floor area of 106.47 m 2 and its performance characteristics are analyzed numerically. To obtain the performance of a hybrid water heating system, the mathematical models of heat pump components and the correlation, which is derived from experimental data of a gas fired water heater, are applied. Also, using the result of a performance calculation, the economic feasibility study is carried out, with the concept of energy cost savings, to quantify the merits of the system and to establish its operation strategy. The monthly electricity and heat consumption data in a Korean residential house and the price of electricity and gas are applied to reflect an actual situation. [ABSTRACT FROM AUTHOR]

In the U.S., heating, ventilating and air conditioning (HVAC) systems are the largest consumers of electrical energy and a major contributor to peak demand. To reduce both peak load and energy cost, the set-point temperature of HVAC can be controlled depending on the electricity price. This paper presents a proposed controller that curtails peak load as well as saves electricity cost while maintaining reasonable thermal comfort. The controller changes set-point temperature when the retail price is higher than customers preset price. To evaluate the performance of the newly developed demand response controller, detailed energy models for two residential buildings are developed to analyze HVAC power consumption for different house sizes and floor plans. The house models are assumed to be located in Austin, Texas, USA and generated with OpenStudio and EnergyPlus. The design of internal load and occupation schedule are based on a residential energy consumption survey and experimental data by the Pecan Street Project, Austin, TX. In addition, historical data from Austin Energy for residential customers, 2012 is used to calibrate two house models. In addition, this paper uses the historical real-time wholesale price data for the Electricity Reliability Council of Texas (ERCOT) wholesale electricity market to model the two types of real-time tariffs that many utilities in the U.S. currently use to generate dynamic pricing for demand response programs. The simulation results show that our demand response controller could provide up to 10.8% of energy cost savings by using the proposed controller with dynamic pricing. While avoiding significant discomfort due to temperature change. Also, the results present potential for saving considering peak load by 24.7% and total electrical energy saving for HVAC in homes by 4.3% annually. [ABSTRACT FROM AUTHOR]

Increasingly, large-scale computing systems are consuming more power each passing year. As power consumption is on the rise, concern has been raised over the growing implications on power bills, carbon emissions and power supply limitations for data centers. Computing systems use hardware components that are not power proportional and machines comprising large systems tend to be underutilized, resulting in great wastage of energy. Motivated by this fact, researchers aim to improve the energy efficiency of these systems by increasing resource utilization and exploiting system characteristics in order to achieve power proportionality. However, many challenges burden the task of producing a power-aware, energy-efficient large-scale system that provides the same performance as today's systems. Particularly, storage systems are of great concern since storage consumes a large amount of power in the data center. This paper outlines the main problems and challenges of delivering power-efficient storage solutions, and proposes a taxonomy of power-aware techniques. This work aims to provide a detailed exposition of current trends in power-aware storage systems in a comprehensive and organized manner, outlining and comparing current solutions and challenges. [ABSTRACT FROM AUTHOR]

This paper hypothesizes that if day-ahead markets for electricity are efficient, then the day-ahead prices will reflect the processed information and expectations of all market participants regarding the next day's electricity load and thus the prices may be useful in actually predicting the next day's load. We test this hypothesis using data for the PG&E aggregation area in the California ISO. The results provide evidence of a positive and significant relationship between the hourly day-ahead electricity price (relative to the natural gas price) and the subsequent actual hourly load. The reported relationship is sufficiently robust to produce a forecast based on the day-ahead hourly price relative to the price of natural gas, some binary variables, and a number of estimated ARMA disturbances that is considerably more accurate than the ISO's day-ahead forecast. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a multiagent model of Smart Grid where customers communicate with the neighbors and trade electricity to minimize electricity costs. [•] The Demand Side Management strategy proposed in this paper assist the customers make an effective use of their resources. [•] The impact of power transactions within neighboring customers, demand diversity, and load shifting are obtained on the customer and the utility. [•] Different case studies show that customers may save 5–10% with 5% shifting of their peak load. [•] The customers’ electricity costs can be reduced by up to 10% with neighborhood electricity trading and diversified load profiles. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between futures prices of electricity, on the one hand and crude oil, natural gas and coal on the other hand, in the United Kingdom, German and Nordic energy markets. Energy traders and market participants use statistical models in their trading activities. We seek to establish robust cointegration based models as decision tools for energy commodity spread trading. We look at three different markets with different input mixtures, for electricity generation. Using daily futures data from period 2006 to 2012, we find cointegration between UK electricity prices and Coal and Gas, and between Nordic electricity prices and Coal. The spread between German electricity prices and Gas, Oil and Coal prices are also stationary. The consequence of this finding could be a possible trading strategy which involves buying electricity futures and selling gas or coal when the spread is above the mean and reversing the strategy when the spread is below the mean. The suggested strategy assumes a reversion to the mean, which we show takes some time, as displayed by the slow speed of adjustment. [ABSTRACT FROM AUTHOR]

Abstract: In this work, the grid design and optimal allocation of wind and biomass resources for renewable electricity supply chains under uncertainties is studied. Due to wind intermittency, generation of wind electricity is not uniform and cannot be counted on to be readily available to meet the demand. Biomass represents a type of stored energy and is the only renewable resource that can be used for producing biofuels and generating electricity whenever required. However, amount of biomass resources are finite and might not be sufficient to meet the demand for electricity and biofuels. Potential of wind and biomass resources is therefore jointly analyzed for electricity generation. Policies are proposed and evaluated for optimal allocation of finite biomass resources for electricity generation. A stochastic programming model is proposed that optimally balances the electricity demand across the available supply from wind and biomass resources under uncertainties in wind speed and electricity sale price. A case study set in the American Midwest is presented to demonstrate the effectiveness of the proposed model by determining the optimal decisions for generation and transmission of renewable electricity. Sensitivity analysis shows that level of subsidy for renewable electricity production has a major impact on the decisions. [Copyright &y& Elsevier]

Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called "chaos particles optimization (CPSO) weight-determined combination models." These models allow for the weight of the combined model to take values of [-1,1]. In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [-1,1], the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models. [ABSTRACT FROM AUTHOR]

SUMMARY This paper develops a new approach to fairly clear the market and assess the amount of each participant's revenue in a deregulated power system, through modifying the system nodal prices. This approach equalizes the economic profit of each participant group to its fair and rational value. Transco behavior is modeled as an independent entity without direct supervision of an independent system operator, causing the possibility of either making profit or incurring a loss for it. With this method applied, three main results for better system design and operation are produced: The rational and acceptable profit/loss values for all participant groups (producers, customers, and Transco) are determined through fair apportionment of total system profit among them based on their optimality and contribution of profitability to the whole system., The profit/loss value for each of producers and consumers in the system is allocated according to their proportional efficiency and extent of transmission use., Transmission revenue assessment is performed through defining a criterion for evaluating the transmission network optimality., So this approach can correct wrong and unfair economic signals in the previous electricity pricing and participants' revenue assessment methods, presenting such a fairer mechanism for it that each participant's action will be oriented in maximizing the system's total economic efficiency. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents a new approach to coordinate the decisions of transmission and generation capacity expansion planning for a competitive electricity market in which only the generation sector is deregulated. The independent system operator (ISO) as transmission planner has a regulatory role in the strategic behavior of generation companies. To reach coordinated decisions, the model relies on an interactive and iterative process between generation expansion planning and transmission expansion planning. This repetitive process continues until reaching a converging point or fulfilling the stopping criterion assigned by the ISO. In order to consider random outages of network components as well as uncertainty of load and bid prices of generating units, the Monte Carlo simulation method is applied. The simulation results confirm the efficacy of the proposed model in the coordinated generation and transmission planning problem considering the uncertainties. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The high price of electricity in Spain is due to inappropriate price regulation. The design of mechanisms to establish the cost of electricity for consumers does not reflect the cost of the energy mix but the price established in an "energy only" market for coal and gas thermal power plants. This price is insufficient to cover the average costs of renewable plants and thermal power plants. However, while this is insufficient for certain power plants, it is seen to be excessive for others. The situation is such that it may now be stated that the high price of electricity and the Tariff Deficit are the result of the cheap energies rather than the expensive energies. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to present a new pricing method for clean spread options, and to illustrate its main features on a set of numerical examples produced by a dedicated computer code. The novelty of the approach is embedded in the use of a structural model as opposed to reduced-form models which fail to capture properly the fundamental dependencies between the economic factors entering the production process. [ABSTRACT FROM AUTHOR]

The article looks at the energy consumption in Mexico with particular focus on households and analyzes their electricity and fuel demands in terms of households' income, rural and urban disposition, and their residents' age and gender. The author estimates energy consumption patterns per capita, based on his calculation of data from Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH) for 2008, concerning families' expenses on electricity, gas, and firewood.

Carbon capture and storage (CCS) can significantly reduce the amount of CO2 emitted from coal-fired power plants but its operation significantly reduces the plant's net electrical output and decreases profits, especially during times of high electricity prices. An amine-based CCS system can be modified adding amine-storage to allow postponing 92% of all its energy consumption to times of lower electricity prices, and in this way has the potential to effectively reduce the cost of CO2 capture by reducing the costs of the forgone electricity sales. However adding amine-storage to a CCS system implies a significant capital cost that will be outweighed by the price-arbitrage revenue only if the difference between low and high electricity prices is substantial. In this paper we find a threshold for the variability in electricity prices that make the benefits from electricity price arbitrage outweigh the capital costs of amine-storage. We then look at wholesale electricity markets in the Eastern Interconnect of the United States to determine profitability of amine-storage systems in this region. Using hourly electricity price data from years 2007 and 2008 we find that amine storage may be cost-effective in areas with high price variability. [ABSTRACT FROM AUTHOR]

The first Feed-in-Tariff (FiT) program in North America was recently implemented in Ontario, Canada to stimulate the generation of electricity from renewable sources. The life cycle greenhouse gas (GHG) emissions and economics of electricity generation through anaerobic digestion (AD) of household source-separated organic waste (HSSOW) are investigated within the FiT program. AD can potentially provide considerable GHG emission reductions (up to 1 t CO2eq/t HSSOW) at relatively low to moderate cost (-$35 to 160/t CO2eq) by displacing fossil electricity and preventing the emission of landfill gas. It is a cost-effective GHG mitigation option compared to some other FiT technologies (e.g., wind, solar photovoltaic) and provides unique additional benefits (waste diversion, nutrient recycling). The combination of electricity sales at a premium rate, savings in waste management costs, and economies of scale allow AD facilities processing >30,000 t/yr to be cost-competitive against landfilling. However, the FiT does not sufficiently support smaller-scale facilities that are needed as a transition to larger, more economically viable facilities. Refocusing of the FiT program and waste policies are needed to support the adoption of AD of HSSOW, which has not yet been developed in the Province, while more costly technologies (e.g., photovoltaic) have been deployed. [ABSTRACT FROM AUTHOR]

UK-based AFC Energy reports successful operation of its enhanced alkaline fuel cell stack and cartridge, exceeding its targeted 2017 milestones as it moves closer to commercialisation. [ABSTRACT FROM AUTHOR]

Within the framework of the COP21 (Conference of the Parties) agreement, Algeria submitted its Intended Nationally Determined Contribution pledging to reduce carbon emissions by at least 7% by 2030. However, it will be a difficult task to reach this target as total final energy consumption has increased 32% from 2010 to 2014, with the major energy increases being related to electricity use in the residential sector. In this context, the relationship between residential electricity consumption and income is analyzed for Algeria in the period 1970–2013, by estimating a residential electricity consumption per capita demand function which depends on GDP per capita, its squared and cubed terms, the electricity prices, and the goods and services imports. An extended Autoregressive Distributed Lag model (ARDL) was adopted to consider the different growth patterns registered in the evolution of GDP. The estimate results show that the relationships between electricity use and GDP (in per capita terms) present an inverted N-shape, with the second turning point having been reached. Therefore, promoting growth in Algeria could be convenient to reduce the electricity consumption, as a higher income level may allow the use of more efficient appliances. Additionally, renewable energies may be adequate to increase the electricity production in order to cover the increasing residential demand. [ABSTRACT FROM AUTHOR]

In this article, the author discusses evolution of renewable power plants decreasing the costs of electricity from renewable sources in 2016. Topics discussed include decrease in bidding amount for procurement of electricity from off-shore wind power in Europe, fall in solar and wind electricity prices by the end of 2017 and planning research focusing on producing fuels using electricity for making energy-rich molecules for substituting oil products in the transport sector.