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In this article, hourly prices of the Turkish Day Ahead Electricity Market are forecasted by using various univariate electricity price models, then the out-of-sample forecasts are compared with each other and the benchmarks. This article has two main contributions to the literature: Firstly, it provides a factorial Analysis of Variance (ANOVA) as a pre-whitening method of the price series and allows one to work with the stationary residuals series. Secondly, it is the first work, which compares the performances of all important statistical univariate forecast models in the Turkish electricity market. Results indicate the importance of the factorial ANOVA application and the SARIMA model’s success under the given conditions. [ABSTRACT FROM AUTHOR]

Electricity companies have become interested in demand-side resources for reasons of conservation and efficiency. Previous empirical evidence demonstrated that more advanced (nonlinear) price schemes may be successfully used effectively to manage consumers’ electricity demand in highly developed market economies. This article shows that it is as possible in moderately developed countries with less experience with a market economy. Data were taken from a unique field study conducted in a midsize city in central Poland (Kalisz), in which approximately 750 households voluntarily agreed to use time-varying rates, instead of the flat rate previously used. The study is based on a detailed analysis of changes in average usage patterns in 15-minute intervals. On average, consumers reacted to price signals as predicted by the traditional economic model of a rational consumer, the change in usage depended on the rate design. It can be argued that at the initial stage, when nonlinear pricing schemes are introduced, simpler rates are preferred, as consumers may have difficulty understanding new rates. Simplicity seems to be the key factor that helps to overcome possible cognitive problems and behavioral anomalies when consumers are exposed to new pricing practices. [ABSTRACT FROM AUTHOR]

In media coverage, the context of the German energy transition, also referred to asEnergiewende,casts a spotlight not only on various technology options (e.g. wind or coal power) but also on more abstract topics such as security of supply or electricity prices. Thereby, the public’s assessment of energy-related issues may greatly rely on perceived risks. Focusing on the quality of energy-related risk reporting, this contribution therefore is intended to explore the German print and TV media discourse on energy options or topics. In our sample, one in three articles connects an energy option or topic with an evident or potential unwanted event. Although the media’s effect on actual risk perception involves some controversies, researchers tend to criticize the media as being susceptible to framing and for failing to place unwanted events in perspective, e.g. by not presenting the corresponding likelihood of occurrence, which is necessary to define risk. If this critique holds true, accurate public risk assessment is partly hampered because media coverage impliesuncertaintyrather than providing all information available. We examined seven indicators of quality reporting derived from literature research: intensity, likelihood, controllability, desired uncertainty, sensationalism, emotional language, and type of unwanted event. Based on German energy media coverage in 2013, we found a relatively high occurrence of intensity and controllability, whereas likelihood and desired uncertainty were reported less often. By aggregating the indicators into a risk-reporting quality index, we did not observe a poor quality of risk reporting on energy issues. In contrast to previous research, the overall quality of energy-related risk reporting can be assessed as at least moderate, implying that the media depicts risks more precisely than assumed. The occurrence of quality indicators thereby significantly depends on the type of unwanted event rather than on the energy option considered. [ABSTRACT FROM PUBLISHER]

Understanding the mechanisms that drive extreme negative and positive prices in day-ahead electricity prices is crucial for managing risk and market design. In this paper, we consider the problem of understanding how fundamental drivers impact the probability of extreme price occurrences in the German day-ahead electricity market. We develop models using fundamental variables to predict the probability of extreme prices. The dynamics of negative prices and positive price spikes differ greatly. Positive spikes are related to high demand, low supply and high prices the previous days, and mainly occur during the morning and afternoon peak hours. Negative prices occur mainly during the night and are closely related to low demand combined with high wind production levels. Furthermore, we do a closer analysis of how renewable energy sources, hereby photovoltaic and wind power, impact the probability of negative prices and positive spikes. The models confirm that extremely high and negative prices have different drivers, and that wind power is particularly important in relation to negative price occurrences. The models capture the main drivers of both positive and negative extreme price occurrences and perform well with respect to accurately forecasting the probability with high levels of confidence. Our results suggest that probability models are well suited to aid in risk management for market participants in day-ahead electricity markets. [ABSTRACT FROM AUTHOR]

Predicting the electricity market price has an important role in the future decision-makings in energy consumption. In this study, three intelligent combined methods: Elman neural network- Genetic Algorithm (El-GA), Elman neural network-Imperialist Competitive Algorithm (El-ICA) and Elman neural network-Particle Swarm Optimization (El-PSO) are proposed to predict the short-term price of electricity, and are evaluated by different error criteria. The data, which are used in this paper, are daily and related to a one-year period (2006), in the USA. The neural network input in this research is different ahead in time periods of electricity price data. Also, in order to have a better evaluation of the performance of the suggested combined methods, the variance analysis (ANOVA) method is employed; and then, based on the mean and distribution of MMAPE error criteria the appropriate method is selected. In the present study, El-PSO combined method has a better performance as compared to other methods such as El-ICA, El-GA, MLP and SVM. [ABSTRACT FROM AUTHOR]

Macroeconomic modelling results based on relatively varying sample sizes may lead to incoherent results. Such effects have not been adequately understood in the renewable energy literature regarding the European Union (EU). This study focuses on the comparison of results obtained for renewable energy investment drivers (for solar and wind energy investments) on different samples of EU countries, including all EU-27, former EU-15 and 11 high renewable investment EU countries. The study used a random effect panel data modelling approach over the period 1995–2011 for studying the impact of the levelized cost, regulation perception, carbon emissions and climatic condition on wind and solar investments over the three samples. The results demonstrate the importance of trustable regulation schemes to ensure that regulation will not have a significant negative effect on investment, showing also the need to further extend the model to include support schemes as fundamental drivers for investment. [ABSTRACT FROM AUTHOR]

Due to the relevance of fossil fuels in the electricity matrix in Spain, the electrical sector plays a crucial role in mitigation policies such as carbon taxes. Increased prices of electricity can act as an incentive to enhance energy efficiency contributing to CO2abatement. This paper evaluates a tax on electricity consumption (ECT). Focusing on energy efficiency commitments for Spain as established by EU Authorities for Horizon 2020 (H2020), a pricing model was created to assess economic impacts and its effectiveness in meeting this commitment. The analysis was performed by considering two scenarios, without (Scenario 1) and with (Scenario 2) tax recycling between the new tax and employer-paid social security benefits or contributing to price stability. In Scenario 2, the tax reform is achieved with tax recycling, offsetting the introduction of the ECT by reducing employer-paid social security payments. Two alternative restrictions on the tax reform were considered for the simulation of Scenario 2. In the first case, a restriction was imposed to ensure revenue neutrality (2-I). In the second case, the restriction ought to maintain price stability (2-II). Results from different scenarios offer an important range of possibilities for policy decisions. The results show that with a tax rate equal to 1%, there is a remarkable reduction of CO2emissions from the electricity sector, and the same happens with other sectors that the literature identifies as drivers of such emissions. [ABSTRACT FROM PUBLISHER]

The purpose of this work is to present a new methodology and the associated computational tools developed within the U.S. Department of Energy Fuel Cycle Options Campaign to quantify the economic performance of complex nuclear fuel cycles. The levelized electricity cost at the busbar is generally chosen to quantify and compare the economic performance of different base load-generating technologies, including nuclear; the levelized electricity cost is the cost that renders the risk-adjusted discounted net present value of the investment cash flow equal to zero. The work presented here is focused on the calculation of the levelized cost of electricity of fuel cycles at mass balance equilibrium, which is termed levelized cost of electricity at equilibrium (LCAE). To alleviate the computational issues associated with the calculation of the LCAE for complex fuel cycles, a novel approach has been developed. This approach has been termed the island approach because of its logical structure, in which a generic complex fuel cycle is subdivided into subsets of fuel cycle facilities called islands, each containing one and only one type of reactor or blanket and an arbitrary number of fuel cycle facilities. A nuclear economic software tool, NE-COST, written in the commercial programming software MATLAB®, has been developed to calculate the LCAE of complex fuel cycles with the island computational approach. NE-COST has also been developed with the capability to handle uncertainty: the input parameters (both unit costs and fuel cycle characteristics) can have uncertainty distributions associated with them, and the output can be computed in terms of probability density functions of the LCAE. In this paper, NE-COST will be used to quantify, as examples, the economic performance of (a) once-through systems of current light water reactors (LWRs), (b) continuous plutonium recycling in fast reactors (FRs) with drivers and blankets, and (c) recycling of plutonium bred in FRs into LWRs. For each fuel cycle, the contributions to the total LCAE of the main cost components will be identified. [ABSTRACT FROM AUTHOR]

This article presents a new approach to the energy efficiency of households based on smart grid technologies applied to the existing power grid. The idea is to make the electrical appliances capable of actively responding to the condition of the upstream grid,e.g., the electricity price, which, under market environments, is passed to end-use customers through retailers or load aggregators. This both saves on the electricity bill for customers and improves the energy efficiency of the system as a whole. The key element is advanced metering infrastructure, which performs two main functions: (1) computing the so-called control policy for electrical appliances with anticipated data and (2) real-time control based on the above control policy and actual measurements. By stochastic optimization, the proposed scheme is capable of monitoring the uncertainty related to the electricity consumption in households,e.g., temperature and/or time and quantity demanded, etc. In addition, with the physical model taken into account, the problem can be applied to various appliances, including what were considered critical loads. The proposed scheme was tested in heat, ventilation, and air-conditioning systems; the simulation result showed the effectiveness in comparison with the traditional (on/off) scheme based on thermostats. [ABSTRACT FROM PUBLISHER]

In the context of day-ahead or real-time electricity prices, the method of economic model predictive control has been shown to provide expenditure reduction in building HVAC systems, specifically if coupled with active thermal energy storage. However, due to the diurnal nature of electricity prices, these expenditure reductions can only be achieved if the economic model predictive control prediction horizon is sufficiently large, typically greater than 12 h. This work develops an alternative controller known as constrained economic linear optimal control. This constrained economic linear optimal control policy is shown to yield economic performance similar to a large-horizon economic model predictive control policy but also possesses a virtual insensitivity to horizon size. Thus, application of the constrained economic linear optimal control policy will require a fraction of the computational effort while yielding nearly identical economic performance. [ABSTRACT FROM PUBLISHER]

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]

The authors examine factors influencing the efficiency of the electricity market: the quality of the organizational model being followed and the economic environment. They explore the gulf between theories underlying the current market model and actual economic relations shaped by the technological nature of the sector. Alternative models and management mechanisms are proposed along with concrete measures to implement them. [ABSTRACT FROM AUTHOR]

The paper proposes an optimal sizing method of a customer's Battery Energy Storage System(BESS) which aims at managing the electricity demand of the customer to minimize electricity cost under the time of use(TOU) pricing. Peak load limit of the customer and charging and discharging schedules of the BESS are optimized on annual basis to minimize annual electricity cost, which consists of peak load related basic cost and actual usage cost(energy cost). The optimal scheduling is used to assess the maximum cost savings for all sets of candidate capacities of BESS. An optimal size of BESS is determined from the cost reduction rate(hereinafter reduction rate) curves via capacity of BESS. Case study uses real data from industry customer and shows how the proposed method can be employed to optimally design the size of BESS for customer demand management. [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]