Management of public water supply to reduce power cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Wind generation output during cold weather-driven electricity demand peaks in Ireland

Recent cold winters and prolonged periods of low wind speeds have prompted concerns about the increasing penetration of wind generation in the Irish and other northern European power systems. On the combined Republic of Ireland and Northern Ireland system there was in excess of 1.5 GW of installed wind power in January 2010. As the penetration of these variable, non-dispatchable generators increases, power systems are becoming more sensitive to weather events on the supply side as well as on the demand side. In the temperate climate of Ireland, sensitivity of supply to weather is mainly due to wind variability while demand sensitivity is driven by space heating or cooling loads. The interplay of these two weather-driven effects is of particular concern if demand spikes driven by low temperatures coincide with periods of low winds. In December 2009 and January 2010 Ireland experienced a prolonged spell of unusually cold conditions. During much of this time, wind generation output was low due to low wind speeds. The impacts of this event are presented as a case study of the effects of weather extremes on power systems with high penetrations of variable renewable generation.

Electric vehicles and displaced gaseous emission

Electric vehicles (EV) do not emit tailpipe exhaust fumes in the same manner as internal combustion engine vehicles. Optimal benefits can only be achieved, if EVS are deployed effectively, so that the tailpipe emissions are not substituted by additional emissions in the electricity sector. This paper examines the potential contributions that Plug in Hybrid Electric Vehicles can make in reducing carbon dioxide. The paper presents the results of the generation expansion model for Northern Ireland and the Republic of Ireland built using the dynamic programming based long term generation expansion planning tool called the Wien Automatic System Planning IV tool. The model optimizes power dispatch using hourly electricity demand curves for each year up to 2020, while incorporating generator characteristics and certain operational requirements such as energy not served and loss of load probability while satisfying constraints on environmental emissions, fuel availability and generator operational and maintenance costs. In order to simulate the effect of PHEV, two distinct charging scenarios are applied based on a peak tariff and an off peak tariff. The importance and influence of the charging regime on the amount of energy used and gaseous emissions displaced is determined and discussed.

A strategic review of electricity systems models

Electricity systems models are software tools used to manage electricity demand and the electricity systems, to trade electricity and for generation expansion planning purposes. Various portfolios and scenarios are modelled in order to compare the effects of decision making in policy and on business development plans in electricity systems so as to best advise governments and industry on the least cost economic and environmental approach to electricity supply, while maintaining a secure supply of sufficient quality electricity. The modelling techniques developed to study vertically integrated state monopolies are now applied in liberalised markets where the issues and constraints are more complex. This paper reviews the changing role of electricity systems modelling in a strategic manner, focussing on the modelling response to key developments, the move away from monopoly towards liberalised market regimes and the increasing complexity brought about by policy targets for renewable energy and emissions. The paper provides an overview of electricity systems modelling techniques, discusses a number of key proprietary electricity systems models used in the USA and Europe and provides an information resource to the electricity analyst not currently readily available in the literature on the choice of model to investigate different aspects of the electricity system.

A study of the 10% electric vehicles target of the single electricity market

In late 2008, the Government of the Republic of Ireland set a specific target that 10% of all vehicles in its transport fleet be powered by electricity by 2020 in order to meet European Union renewable energy targets and greenhouse gas emissions reduction targets. International there are similar targets. This is a considerable challenge as in 2009, transport accounted for 29% of non-emissions trading scheme greenhouse gas emissions, 32% of energy-related greenhouse gas emissions, 21% of total greenhouse gas emissions and approximately 50% of energy-related non-emission trading scheme greenhouse gas emissions. In this paper the impacts of 10% electric vehicle charging on the single wholesale electricity market for the Republic of Ireland and Northern Ireland is examined. The energy consumed and the total carbon dioxide emissions generated under different charging scenarios is quantified and the results of the charging scenarios are compared to identify the best implementation strategy.

An Analysis of the Effect of Electric Vehicle Charging on the Operation of the Single Electricity Market

To meet European Union renewable energy and greenhouse gas emissions reduction targets the Irish government set a target in 2008 that 10% of all vehicles in the transport fleet be powered by electricity by 2020. Similar electric vehicle targets have been introduced in other countries. However, reducing energy consumption and decreasing greenhouse gas emissions in transport is a considerable challenge due to heavy reliance on fossil fuels. In fact, transport in the Republic of Ireland in 2009 accounted for 29% of non-emissions trading scheme greenhouse gas emissions, 32% of energy-related greenhouse gas emissions, 21% of total greenhouse gas emissions and approximately 50% of energy-related non-emission trading scheme greenhouse gas emissions. In this paper the effect of electric vehicle charging on the operation of the single wholesale electricity market for the Republic of Ireland and Northern Ireland is analysed. The energy consumed, greenhouse gas emissions generated and changes to the wholesale price of electricity under peak and off-peak charging scenarios are quantified and discussed. Results from the study show that off-peak charging is more beneficial than peak charging.

Residential consumption of gas and electricity in the U.S.: The role of prices and income

We study the residential demand for electricity and gas, working with nationwide household-level data that cover recent years, namely 1997-2007. Our dataset is a mixed panel/multi-year cross-sections of dwellings/households in the 50 largest metropolitan areas in the United States as of 2008. We estimate static and dynamic models of electricity and gas demand. We find strong household response to energy prices, both in the short and long term. From the static models, we get estimates of the own price elasticity of electricity demand in the -0.860 to -0.667 range, while the own price elasticity of gas demand is -0.693 to -0.566. These results are robust to a variety of checks. Contrary to earlier literature (Metcalf and Hassett, 1999; Reiss and White, 2005), we find no evidence of significantly different elasticities across households with electric and gas heat. The price elasticity of electricity demand declines with income, but the magnitude of this effect is small. These results are in sharp contrast to much of the literature on residential energy consumption in the United States, and with the figures used in current government agency practice. Our results suggest that there might be greater potential for policies which affect energy price than may have been previously appreciated. (C) 2011 Elsevier B.V. All rights reserved.

Response of residential electricity demand to price: The effect of measurement error

In this paper we present an empirical analysis of the residential demand for electricity using annual aggregate data at the state level for 48 US states from 1995 to 2007. Earlier literature has examined residential energy consumption at the state level using annual or monthly data, focusing on the variation in price elasticities of demand across states or regions, but has failed to recognize or address two major issues. The first is that, when fitting dynamic panel models, the lagged consumption term in the right-hand side of the demand equation is endogenous. This has resulted in potentially inconsistent estimates of the long-run price elasticity of demand. The second is that energy price is likely mismeasured.