Peak Demand

ABTRACT This paper goals at studying the place and possible contribution of "Internet of Things" (IoT) in the context of the EU's ambitious climate and energy targets for 2020. Using qualitative procedure, we are mainly concentrating on Demand Side Management (DSM) as an effective method in balancing the load of Electrical Distribution Networks. The role of IoT in DSM is to enable and enhance electrical energy peak demand reduction and its maximum uniform time-distribution achieved through society's eco-education. Using computational tools such as Data Mining and Recommender System we can achieve results at the level of electrical energy distribution network reflected in peak reduction and its uniform time distribution.

Demand-side flexibility has been suggested as a tool for peak demand reduction and large-scale integration of low-carbon electricity sources. Deeper insight into the activities and energy services performed in households could help to understand the scope and limitations of demand-side flexibility. Measuring and Evaluating Time-and Energy-use Relationships (METER) is a 5-year, UK-based research project and the first study to collect activity data and electricity use in parallel at this scale. We present statistical analyses of these new data, including more than 6250 activities reported by 450 individuals in 173 households, and their relationship to electricity use patterns. We use a regular-ization technique to select influential variables in regression models of average electricity use over a day and of discretionary use across 4-h time periods to compare intra-day variations. We find that dwelling and appliance variables show the strongest associations to average electricity consumption and can explain 49% of the variance in mean daily usage. For models of 4-h average Bde-minned^ consumption, we find that activity variables are consistently influential, both in terms of coefficient magnitudes and contributions to increased model explanatory power. Activities relating to food preparation and eating, household chores, and recreation show the strongest associations. We conclude that occupant activity data can advance our understanding of the temporal characteristics of electricity demand and inform approaches to shift or reduce it. We stress the importance of considering consumption as a function of time of day, and we use our findings to argue that a more nuanced understanding of this relationship can yield useful insights for residential demand flexibility.

With different intensities, depending on the season, every morning and evening of any weekday there are the same peaks in electricity demand. Peaks are problematic for system balancing, utilities’ pricing and future grid development. The volume of energy demand depends on many factors, including weather, type of appliances, types of building, etc, but people, their routines and practices are the key to understand what constitutes peak demand.

This paper investigates the forecasting of a large fluctuating seasonal demand prior to peak sale season using a practical time series, collected from the US Census Bureau. Due to the extreme natural events (e.g. excessive snow fall and calamities), sales may not occur, inventory may not replenish and demand may set off unrecorded during the peak sale season. This characterises a seasonal time series to an intermittent category. A seasonal autoregressive integrated moving average (SARIMA), a multiplicative exponential smoothing (M-ES) and an effective modelling approach using Bayesian computational process are analysed in the context of seasonal and intermittent forecast. Several forecast error indicators and a cost factor are used to compare the models. In cost factor analysis, cost is measured optimally using dynamic programming model under periodic review policy. Experimental results demonstrate that Bayesian model performance is much superior to SARIMA and M-ES models, and efficient to forecast seasonal and intermittent demand.

An optimization method is proposed to size a compact solar domestic hot water system (CSDHWS) for low-income families with multiple hot water load profiles. The Life Cycle Savings (LCS) optimization is carried out using the Transient Simulation Program (TRNSYS) with the Generic Optimization Program (GenOpt) for Florianópolis (27.6S, 48.5W), Brazil. A total of seven variables are simultaneously optimized. Results can be used to locally optimize CSDHWS’s based on meteorological data. The proposed optimization method is an effective measure to reduce peak demand as well as total energy consumption.

ABSTRACT In this paper, a detailed simulation-based analysis is conducted to assess the impact of adopting Daylight Saving Time (DST) on the electrical energy use and peak demand in Kuwait. The analysis focused on the impact of DST in the building sector since it represents 90% of electrical energy usage of Kuwait. The simulation results indicate that the adoption of DST has mixed impacts for Kuwait. While the commercial and the governmental sectors may benefit from the DST, the private residences and apartment buildings can see both their annual energy use and peak demand increase slightly by adopting DST. The overall impact of the DST implementation is rather minimal with a slight increase energy use of about 0.07% and a slight reduction in peak demand of 0.14% or about 12Â MW based on 2005 electrical peak demand for Kuwait.

Electricity prices in Egypt have been set significantly lower than the real economic cost of its production and supply. These subsidies encourage the waste of energy and increase the fluctuation in demand, triggering a huge need of additional power generation capacity in Egypt. This dialectical paper addresses this problem by first theoretically analyzing the Egyptian electricity market and then discussing