Climate policy costs of spatially unbalanced growth in electricity demand: the case of datacentres. ESRI Working Paper No. 657 March 2020

We investigate the power system implications of the anticipated expansion in electricity demand by datacentres. We perform a joint optimisation of Generation and Transmission Expansion Planning considering uncertainty in future datacentre growth under various climate policies. Datacentre expansion imposes significant extra costs on the power system, even under the cheapest policy option. A renewable energy target is more costly than a technology-neutral carbon reduction policy, and the divergence in costs increases non-linearly in electricity demand. Moreover, a carbon reduction policy is more robust to uncertainties in projected demand than a renewable policy. High renewable targets crowd out other low-carbon options such as Carbon Capture and Sequestration. The results suggest that energy policy should be reviewed to focus on technology-neutral carbon reduction policies

WHO PAYS FOR RENEWABLES? THE EFFECT OF DATACENTRES ON RENEWABLE SUBSIDIES. ESRI Research Bulletin 2019/11

Ireland faces several targets for renewable energy usage, across the heating, transport and electricity sectors. These targets are set as a proportion of total energy usage. In the case of electricity, 40% of electricity must be generated from renewable sources by 2020. To meet this target, renewable electricity generation is subsidised through the Public Service Obligation levy, which appears on all consumers’ bills. The PSO is levied on residential consumers, commercial consumers and large industrial consumers according to their contribution to peak demand – the more the sector contributes to peak demand, the higher the portion of PSO that they pay

Who pays for renewables? Increasing renewable subsidisation due to increased datacentre demand in Ireland. ESRI WP566, June 2017

Demand from datacentres makes up a rapidly growing portion of electricity demand in Ireland. Increased demand in turn gives rise to increased renewable generation, mandated by government targets, and a corresponding increase in subsidisation levels. The current method of apportioning renewable subsidy costs may lead to consumers other than datacentres bearing this excess cost of subsidisation. This letter calculates the expected impact on these consumers

Distributional impacts of carbon taxation and revenue recycling: a behavioural microsimulation. ESRI WP626, June 2019

Carbon taxation is a regressive policy which contributes to public opposition towards same. We employ the Exact Affine Stone Index demand system to examine the extent to which carbon taxation in Ireland reduces emissions, as well as its distributional impacts. The Engel curves for various commodity groupings are found to be non-linear, which renders the particular demand system we have chosen more suitable than other methods found in the extant literature. We find that a carbon tax increase can decrease emissions, but is indeed regressive. Recycling the revenues to households mitigates these regressive effects. A targeted allocation that directs the revenues towards less affluent households is found to reduce inequality more than flat allocation that divides the revenues equally amongst all households; however both methods are capable of mitigating the regressive effects of the tax increase

Re-evaluating Irish energy policy in light of Brexit. ESRI Research Notes 2014/2/1

The result of the UK referendum on EU membership has prompted a re-evaluation of many Irish policies with a view to ‘Brexit-proofing’ them. The areas of energy and climate policy are no different. As things stand, much of Irish energy and climate policy is shaped at EU level, and so the UK leaving the EU would have implications for Irish policy irrespective of the strong ties between the Irish and UK energy systems. Re-evaluation of Irish energy policy in light of Brexit is therefore understandable and advisable. However, many issues facing Irish, and indeed EU, energy and climate policy are independent of Brexit, and should not be neglected in the public debate. This paper briefly examines some of these issues, with a particular view as to whether and how the policy context has changed in light of Brexit

Capacity-constrained renewable power generation development in light of storage cost uncertainty. ESRI Working Paper No. 647 December 2019

The development of sustainable energy sources and their enabling infrastructures are often met by public opposition, resulting in lengthy planning processes. One proposed means of reducing public opposition is constraining the capacity of renewable energy projects onshore, leading to more small-scale, decentralised and possibly community-driven developments. This work computes the effects of same by performing a medium- and long-term generation expansion planning exercise considering two renewable development cases, in which renewable power expansion is spatially constrained to certain degrees, under high and low storage cost regimes. We employ an appropriately designed optimisation model, accounting for network effects, which are largely neglected in previous studies. We apply our study to the future Irish power system under a range of demand and policy scenarios. Irrespective of storage costs, the unconstrained portfolio is marginally cheaper than the constrained one. However, there are substantial differences in the final generation expansion portfolios. The network reinforcement requirements are also greater under the unconstrained approach. Lower storage costs only slightly mitigate the costs of capacity constraints but significantly alter the spatial distribution of generation investments. The differential in costs between the unconstrained and constrained cases increases non-linearly with renewable generation targets

The Effects of Wind Generation Capacity on Electricity Prices and Generation Costs: a Monte Carlo Analysis. ESRI WP494. November 2014

We use Monte Carlo analysis to examine the potential of increased renewable generation to provide a hedge against variability in energy prices and costs. Fuel costs, electricity demand and wind generation are allowed to vary and a unit commitment and economic dispatch algorithm is employed to produce cost- minimising generation schedules under different levels of installed wind capacity. Increased wind capacity reduces the mean and the variance of production costs but only the variance of electricity prices. Wind generators see their market revenues increase while consumer payments and fossil generator profits do not considerably vary as wind capacity increases. Risk aversion is captured by considering the Conditional Value-at-Risk for both consumers and producers. The optimal level of wind generation increases as risk aversion increases due to the potential of wind to act as a hedge against very high electricity prices in high fuel price scenarios

Carbon taxation in Ireland: Distributional effects of revenue recycling policies. Quarterly Economic Commentary Special Article, Summer 2019.

We calculate the impact of an increase in carbon taxation on carbon emissions and on income inequality. Carbon emissions reduce by 3.94 per cent for a carbon tax increase of €30 per tonne, and 10.24 per cent for an increase of €80 per tonne. Carbon taxation is found to be regressive, with poorer households spending a greater proportion of their income on the tax than more affluent households. However, returning the carbon tax revenues to households reverses this regressive effect, and the net policy effect is progressive. A ‘carbon cheque’ that distributes the revenues equally to every household leads to small changes in income inequality, while a targeted mechanism that directs more of the revenues towards less affluent households is more progressive, and actually reduces income inequality. The targeted mechanism resembles recycling the revenues through the tax and welfare system, and thus has lower administrative costs than a ‘carbon cheque’

Demand response within the energy-for-water-nexus - A review. ESRI WP637, October 2019

A promising tool to achieve more flexibility within power systems is demand re-sponse (DR). End-users in many strands of industry have been subject to research up to now regarding the opportunities for implementing DR programmes. One sector that has received little attention from the literature so far, is wastewater treatment. However, case studies indicate that the potential for wastewater treatment plants to provide DR services might be significant. This review presents and categorises recent modelling approaches for industrial demand response as well as for the wastewater treatment plant operation. Furthermore, the main sources of flexibility from wastewater treatment plants are presented: a potential for variable electricity use in aeration, the time-shifting operation of pumps, the exploitation of built-in redundan-cy in the system and flexibility in the sludge processing. Although case studies con-note the potential for DR from individual WWTPs, no study acknowledges the en-dogeneity of energy prices which arises from a large-scale utilisation of DR. There-fore, an integrated energy systems approach is required to quantify system and market effects effectively

The effects of wind generation capacity on electricity prices and generation costs: A Monte Carlo analysis

We use Monte Carlo analysis to examine the potential of increased renewable generation to provide a hedge against variability in energy prices and costs. Fuel costs, electricity demand and wind generation are allowed to vary and a unit commitment and economic dispatch algorithm is employed to produce costminimising generation schedules under different levels of installed wind capacity. Increased wind capacity reduces the mean and the variance of production costs but only the variance of electricity prices. Wind generators see their market revenues increase while consumer payments and fossil generator profits do not considerably vary as wind capacity increases. Risk aversion is captured by considering the Conditional Value-at-Risk for both consumers and producers. The optimal level of wind generation increases as risk aversion increases due to the potential of wind to act as a hedge against very high electricity prices in high fuel price scenarios