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

Flexible heat pumps: must-have or nice to have in a power sector with renewables?

Heat pumps are a key technology for reducing fossil fuel use in the heating sector. A transition to heat pumps implies an increase in electricity demand, especially in cold winter months. Using an open-source power sector model, we examine the power sector impacts of a massive expansion of decentralized heat pumps in Germany in 2030, combined with buffer heat storage of different sizes. Assuming that the additional electricity used by heat pumps has to be fully covered by renewable energies in a yearly balance, we quantify the required additional investments in renewable energy sources. If wind power expansion potentials are limited, the roll-out of heat pumps can also be accompanied by solar PV with little additional costs, making use of the European interconnection. The need for additional firm capacity and electricity storage generally remains limited even in the case of temporally inflexible heat pumps. We further find that relatively small heat storage capacities of 2 to 6 hours can substantially reduce the need for short- and long-duration electricity storage and other generation capacities, as well as power sector costs. We further show that 5.8 million additional heat pumps save around 120 TWh of natural gas and 24 million tonnes of CO$_2$ emissions per year

Models of demand response and an application for wastewater treatment plants1. ESRI Research Bulletin February 2020/04

Demand response can be defined as any change of the usual electricity demand pattern in response to a price signal from the electricity supplier. It is widely seen as a promising tool to increase energy system flexibility: electricity demand can increase when there is a surplus of electricity available, such as when wind levels are high, and can reduce when there is a shortage of electricity. In the industrial sector in particular, the potential for demand response can be significant. This is because electricity costs can be a big share of total costs and therefore there is a strong incentive to reduce electricity expenditures in order to be competitive. However, to date, the demand response from industrial electricity users has only been examined in a generic way, without taking account of their specific characteristics. Any results arising from these examinations are therefore of limited use for policy makers and industry participants

Water-Energy Nexus: Analysing the energy-for-water relationship in integrated energy systems

The volatility of renewable energies poses challenges to power system reliability and calls for more flexible electricity resources, both on the supply and the demand side. Energy-intensive water services such as wastewater treatment offer great demand flexibility potential in that regard. However, current demand response modelling approaches are insufficient for assessing this potential accurately. This study aims to fill the knowledge gap in industrial demand response modelling by introducing an integrated energy-water system model, which takes into account the constraints of the wastewater treatment process on power system scheduling in a joint system dispatch problem. The model is applied to a case study of the Irish wastewater treatment sector and power system. The objective of this study is to identify the benefits of energy demand and supply flexibility of wastewater treatment plants for power system operation, wastewater treatment operators and electricity consumers. The findings indicate that the wastewater treatment sector can be a valuable demand response resource for the power system. Wastewater treatment operators, electricity consumers and power system operators benefit from more flexible electricity demand from wastewater treatment plants, even in the presence of other flexibility measures in the system. Furthermore, it decreases the carbon intensity of domestic power generation. There is also a benefit for the power system operator in harnessing the flexibility of demand response and biogas production simultaneously. However, this can result in temporarily high electricity prices in the model, leading to increased electricity costs for consumer and wastewater treatment plants. Two main conclusions can be drawn from the findings of this study. First, wastewater treatment plants have untapped potential for demand response and utilising it for power system flexibility benefits wastewater treatment operators, electricity consumers and power system operators. The results inform policy makers on how to evaluate and support the electricity demand and supply flexibility of wastewater treatment plants. Given the benefits and minimal capital costs, policy makers should incentivise WWTP operators to tap into this readily available flexibility potential. Further, policy makers should carefully select the appropriate support schemes. In particular, smart demand response schemes should take into account possible interactions with electricity supply flexibility from biogas generation. Second, including wastewater treatment constraints in the system dispatch problem is crucial in order to estimate the flexibility potential accurately and uncover bottlenecks, which would probably be concealed by a black-box approach. Thus, this study provides a valuable case study for investigating the demand response potential of highly complex industrial processes, such as wastewater treatment

Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energywater nexus

A promising tool to achieve more flexibility within power systems is demand response (DR). End users in many strands of industry have been subject to research regarding the opportunities for implementing DR programmes. We review recent DR modelling approaches in the realm of energy systems models and industrial process models. We find that existing models over- or underestimate the available DR potential from an industrial end user for two main reasons. First, the interaction between power system operation and industrial process operation caused by DR is not taken into account. Second, models abstract from critical physical process constraints affecting the DR potential. To illustrate this, we discuss the wastewater treatment process as one industrial end user within the energy-water nexus, for which the lack of suitable modelling tools is affecting the accurate assessment of the DR potential. Case studies indicate the potential for wastewater treatment plants to provide DR, but no study acknowledges the endogeneity of energy prices which arises from a large-scale utilisation of DR. Therefore, we propose an integrated modelling approach, combining energy system optimisation with the level of operational detail in process simulation models. This will yield a higher level of accuracy regarding the assessment of DR potential from a specific process, such as wastewater treatmen

Backbone

Backbone is a generic energy network optimization tool written in GAMS. It has been designed to be highly adaptable in different dimensions: temporal, spatial, technology representation and market design. The model can represent stochastics with a model predictive control method, with short-term forecasts and longer-term statistical uncertainties. Backbone can support multiple different models due to the modifiable temporal structure and varying lengths of the time steps.Backbone is available at https://gitlab.vtt.fi/backbone/backbone.If you use Backbone in a published work, please cite the following publication, which describes the Backbone energy systems modelling framework https://doi.org/10.3390/en12173388.Releases:v1.2 Nov 12, 2019v1.1 Jun 10, 2019<br/