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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]

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]

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]

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]

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.