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SUMMARY: In this paper, a hybrid model of fuzzy clustering and ANN (Artificial Neural Network) is proposed for electricity price forecasting. Due to the complicated behavior of electricity price in power markets, market players are interested in maximizing profits while minimizing risks. As a result, more accurate models are required to deal with electricity price forecasting. This paper proposes a new method that makes use of fuzzy clustering preconditioned GRBFN (Generalized Radial Basis Function Network) to provide more accurate predicted prices. Fuzzy clustering plays a key role to prevent the number of learning data from decreasing at each cluster. GRBFN is one of efficient ANNs to approximate nonlinear systems. Furthermore, a modified GRBFN model is developed to improve the performance of GRBFN with the use of DA (Deterministic Annealing) clustering for the parameters initialization and EPSO (Evolutionary Particle Swarm Optimization) for optimizing the parameters of GRBFN. The proposed method is successfully applied to real data of ISO New England, USA. [ABSTRACT FROM AUTHOR]

SUMMARY There is a danger of output suppression of high-penetration residential photovoltaic systems due to voltage increase. It is necessa1ry to install new technology to prevent the occurrence of such phenomenon. Therefore, we focused our attention on heat-pump water heaters (HPWHs). HPWHs are usually used to heat water during nighttime because electricity prices are cheaper than during the daytime for the load leveling in Japan. So they can be used as a countermeasure without additional cost if they are operated during the daytime. However, HPWHs do not have sufficient capacity to absorb inverse energy at each residence. Thus, HPWH operation must be optimized to minimize output suppression loss. In this research, we selected four typical sunny days in spring, summer, autumn, and winter. The optimal HPWH operation was calculated by numerical simulation. The optimal monthly HPWH operation was investigated using the weather forecast assuming actual operation in each season. [ABSTRACT FROM AUTHOR]

This paper uses high-frequency continuous intraday electricity price data from the EPEX market to estimate and forecast realized volatility. Three different jump tests are used to break down the variation into jump and continuous components using quadratic variation theory. Several heterogeneous autoregressive models are then estimated for the logarithmic and standard deviation transformations. Generalized autoregressive conditional heteroskedasticity (GARCH) structures are included in the error terms of the models when evidence of conditional heteroskedasticity is found. Model selection is based on various out-of-sample criteria. Results show that decomposition of realized volatility is important for forecasting and that the decision whether to include GARCH-type innovations might depend on the transformation selected. Finally, results are sensitive to the jump test used in the case of the standard deviation transformation. [ABSTRACT FROM AUTHOR]

Grid connected electrical energy storage is expected to enable the integration of variable renewable generation in the future. As the electricity sector develops wholesale electricity prices will change, which will change the way in which storage technologies are operated. This study investigates the sensitivity of storage revenue to uncertain market variables. Results indicate that higher gas prices, carbon prices and average demand would increase peak electricity prices, leading to larger daily price spreads and increased storage revenue. Increased wind generation, however, would reduce opportunities for price arbitrage and lessen storage revenue. Wind power also affects the way in which devices are operated and changes the characteristics which are rewarded by the market. With increased wind capacity, storage devices cycle less regularly as operation is driven by substantial changes in wind power output, rather than daily demand patterns. As a result, slower discharge times are more favourable and revenue is more sensitive to rates of self-discharge. Furthermore, there is less variation in wholesale electricity price and consequently conversion efficiency is more critical to performance. [ABSTRACT FROM AUTHOR]

Renewable Portfolio Standards ( RPS) are U. S. state mandates that utilities produce some of their electricity using renewable energy sources in an effort to reduce greenhouse gas emissions. While advocates highlight the potential long-term benefits of RPS, critics argue that RPS will increase electricity prices due to the higher costs of renewable energy generation. However, to date, there are no published empirical studies of the effect of RPS on electricity prices. Using state-level panel data from 1990 to 2011 and the difference-in-differences method, I find that implementation increases electricity prices when the RPS policy first becomes binding. [ABSTRACT FROM AUTHOR]

Additional flexibilities on the demand side can be obtained by using price signals to change the consumption behavior of household electricity customers. The present contribution proposes a new theoretically motivated demand response model type called virtual storage. First, the basic model structure of several virtual storage models is introduced. All of these models are based on a system of difference equations that describe load reductions/increases in response to price signals. The virtual storage models differ thereafter in how past or prognosis-based future price information is considered. After a description of a proposed model validation strategy, the model behavior of several virtual storage models is compared with some of the common demand response model types and with real customer responses to a price signal. Thus, a model comparison is performed on the basis of a real smart meter data set (Olympic Peninsula Project). [ABSTRACT FROM AUTHOR]

With widespread installation of distribution generations (DG) mainly in distribution networks, a fairly incentive scheme is required to better meet the investment and innovation challenge and to drive the innovation needed to deliver a sustainable energy network with value for money to existing and future consumers. This study proposes a new method to determine DG energy price based on its impact on active loss reduction. This method calculates the actual value of each DG in the active loss reduction and according to this value, determines DG energy price, which is named value based pricing. The proposed approach is applied to a market model with two different agents, namely considered as the distribution company (DisCo) and the owner of DG. In this model, the DisCo is considered as the sole owner and operator of a distribution network and can purchase energy from the wholesale electricity market and/or from DG units owned by independent producers. To illustrate the advantages of the proposed method compared with the local marginal pricing(, both methods are implemented in 33-bus distribution network. [ABSTRACT FROM AUTHOR]

Studies of the aggregated hourly electricity load in geographical areas typically show a systematic variation over the day, the week, and seasons. With hourly metering of individual customers, data for individual consumption profiles have become available. Looking into these data we show that consumption profiles for specific categories of customers are equally systematic but quite distinct for different categories of customers. That is, different categories of customers contribute quite differently to the aggregated load profile. Coupling consumption profiles with hourly market prices which also include a systematic component in the hourly variation, we show that customers with different consumption profiles experience different average cost of their electricity consumption when billed according to hourly time-of-use prices. Thus, some categories of customers stand to gain from time-of-use pricing, while others stand to lose. In Denmark, typically industry, private services and households stand to lose, whereas agriculture and public services stand to gain from time-of-use pricing. However, differences within categories of customers are considerable and, for example, industrial companies running 24 h a day tend to gain from a time-of-use pricing. WIREs Energy Environ 2014, 3:582-593. doi: 10.1002/wene.120 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [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]

We develop an empirical model of the determinants of end-use energy prices in 28 Organisation for Economic Co-operation and Development countries. For this, we construct a unique panel data-set and formulate a spatial panel econometric model with fixed country effects for the period of 1980-2009. A special feature of the proposed empirical model is that cross-country interdependence in energy prices surfaces through two channels, a metric of similarity towards energy policy and a metric of trade in energy goods. We estimate the parameters of that model and conduct several comparative static experiments. We find that higher income per capita and lower efficiency in energy distribution tend to raise energy prices whereas higher net imports of energy and more abundant energy resource endowments reduce them. We also find that bilateral trade in energy goods is an important channel of transmission of shocks in energy prices across countries. The estimation results suggest that countries that use the same types of resources to generate energy are likely to transmit energy price shocks to each other through competition effects. [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.