Space-time Modeling of Electricity Spot Prices

Abstract

In this paper we derive a space-time model for electricity spot prices. A general spatial Durbin model that incorporates the temporal as well as spatial lags of spot prices is presented. Joint modeling of space-time eects is necessarily important when prices and loads are determined in a network of power exchange areas. We use data from the Nord Pool electricity power exchange area bidding markets. Dierent spatial weight matrices are considered to capture the structure of the spatial dependence process across dierent bidding markets and statistical tests show signicant spatial dependence in the spot price dynamics. Estimation of the spatial Durbin model show that the spatial lag variable is as important as the temporal lag variable in describing the spot price dynamics. We use the partial derivatives impact approach to decompose the price impacts into direct and indirect eects and we show that price eects transmit to neighboring markets and decline with distance. In order to examine the evolution of the spatial correlation over time, a time varying parameters spot price spatial Durbin model is estimated using recursive estimation. It is found that the spatial correlation within the Nord Pool grid has been increasing over time which we interpret as evidence for an increasing degree of market integration.

Prevalence and Associated Risk Factors of Gastrointestinal Nematodes of Sheep and Goats in ZiwayDugda District, Eastern Arsi Zone of Oromia Regional State, Ethiopia

“…Many research findings showed that gastrointestinal nematodes are the major causes of losses in production and productivity of small ruminant production in Ethiopia [15].The present study revealed the presence of GIT nematode parasites with an overall prevalence of 71.88% in sheep and goats. Even the result was relatively higher than the reports of other researchers [27,28,29]who reported 54.1%, 47.67% and 43.2% prevalence, respectively. The current finding is also slightly greater than the prevalence of 59.89% and 68.1% reported by [30,31] from different parts of Ethiopia respectively.…”

“…This indicated that male and female sheep have equal chance of infection if they are exposed to the same contaminated communal grazing pasture. However, [28] reported that female animals are more susceptible to parasitism. It is assumed that females are more prone to parasitism especially during pregnancy and peri-parturient period due to both stress and decreased immune status [42].…”

Spatial Effects of Wind Generation and Its Implication for Wind Farm Investment Decisions in New Zealand

“…They concluded that forecasts of electricity prices should incorporate the effects of spatial correlation. Abate and Haldrup (2017) applied a space-time Durbin model to estimate Nord Pool daily electricity spot prices. Their results also confirm the importance of spatial dependence when forecasting over longer time horizons.…”

“…7 With the contiguity weight matrix, we treat nodes are neighbours if there is a transmission cable connecting them. To better capture power flow through the electricity network and examine the interaction between nodes, according to the approach proposed by Douglas and Popova (2011), and adopted by Abate and Haldrup (2017), we construct the transmission weight matrix where the non-diagonal elements are calculated based on the transmission line capacity, which is labelled in Figure 1 ( Young et al, 2012).…”

Regional electricity market price forecasting based on an adaptive spatial–temporal convolutional network

“…The results showed that deep learning methods generally have better accuracy than statistical models. Afrasiabi et al (2019) applied wind speed and residential load forecasts as auxiliary inputs for electricity price prediction and proposed a deep learning algorithm to estimate the probability density of electricity prices. Zhang et al (2020) proposed an adaptive hybrid model based on variational mode decomposition (VMD), self-adaptive particle swarm optimization (SAPSO), seasonal autoregressive integrated moving average (SARIMA) network, and deep belief network (DBN) to predict electricity market prices.…”