Nicholas Good

Physical modeling of electro-thermal domestic heating systems with quantification of economic and environmental costs

Electro-thermal domestic heating systems represent excellent potential sources for demand response and demand side flexibility given the large amount of storage inherent in them. However, they must be modeled using an approach which adequately captures the detailed physics of the problem (in order to properly assess the different sources of inherent storage), and yet is simple enough to be generalizable to any building and heating type that could be available in the future. This paper presents a comprehensive physical modeling approach for electro-thermal domestic heating systems, in which space heating demand derived from the physical modeling approach is combined with domestic hot water and electricity demand to demonstrate how quantification of economic and environmental costs of different domestic heating systems as well as of the thermal inertia of different components (for future demand response studies) may be undertaken.

Modelling and assessment of business cases for smart multi-energy districts

This work presents a stochastic mixed integer linear program based model, for optimization and business case assessment of smart multi-energy districts (including electricity, heat and gas). The model is general and extensible, and can include multiple types of multi-energy generation and consumption. In particular, it is capable of co-optimization of energy and capacity, for participation in multiple energy/reserve/capacity markets. Further, the model incorporates a level-of-aggregation approach, which facilitates modelling and assessment of physical and virtual aggregation within districts. The model is demonstrated through application to a case study district in the Irish energy system. Prices from the various relevant energy markets and charging regimes are presented, before the results of optimization with respect to various business cases are explored. The value of optimization on retail prices, various energy/capacity-related markets/charging regimes, and of aggregation is demonstrated. Directions for further application of the model are detailed.

Participation of electric heat pump resources in electricity markets under uncertainty

This paper presents a model for calculating the optimal purchasing strategy in a day ahead market for an aggregation of domestic buildings utilising electric heat pumps (EHP) to supply low grade thermal energy (for space heating and domestic hot water). The model includes physical models of buildings and of thermal energy stores (TES). Uncertainty in outdoor temperature (hence space heating demand and imbalance volume) and imbalance prices is modelled using a stochastic programming approach, whilst uncertainty in non-heating electricity and domestic hot water demand, and building occupancy (which is a determinant of space heating demand) is accounted for through random assignation of synthetic profiles to buildings/scenarios. The effect on the purchasing costs of the presence and size of a TES and the effect of the size of the EHP are tested.

Exploring flexibility of aggregated residential electric heat pumps

The integration of Renewable Energy Sources (RES) and the electrification of the heating and transportation sectors are stressing the operation of current power systems and call for more flexibility. Domestic electric heat pumps (EHP), which are expected to be widely deployed in the future, can be considered as one potential source of such system flexibility. However, this can also lead to negative impacts for building occupant comfort and to increased peak demand, through reduction in load diversity. Such impacts may be mitigated through the deployment of Thermal Energy Storage (TES), although the benefit this brings is not well understood. Therefore, this paper presents a method to quantify the impact on occupant comfort level and load diversity, through various payback metrics. A validated model is then used to simulate the extraction of reserve capacity from a cluster of 500 domestic buildings with EHPs and different configurations of space heating buffer. Performance in terms of occupant comfort and payback is evaluated.

Energy efficiency at the building and district levels in a multi-energy context

Increasing environmental concerns are encouraging new building energy efficiency (EE) concepts defined as reductions in net consumption of electricity, heat, gas and/or other energy vectors. Thanks to the smart grid paradigm, this is increasingly being achieved via the installation of low carbon technologies. However, this approach can be severely limited due to the physical and technical constraints of particular buildings (e.g., limited space to install technologies and limited energy network connection capacity). This paper proposes a more effective approach by explicitly including all relevant multi-energy flows in the EE concept and extending its scope to the district level. This paradigm shift allows energy flows to be produced and consumed in the most effective locations. The benefits from extending the scope of different EE concepts (e.g., based on electricity, electricity and heat, and all vectors) from the building to the district level are illustrated with a real UK multi-energy system.

Flexibility in multi-energy communities with electrical and thermal storage: A stochastic, robust approach for multi-service demand response

There is increasing interest in multi-energy communities, which could become important sources of demand response flexibility, especially when equipped with storage. Their location on distribution networks mean their exploitation to solve local capacity congestions may be particularly valuable, whilst their ability to partake in energy/reserve markets can improve their business cases. However, maximizing this flexibility potential by providing multiple services that are subject to uncertain calls is a challenging modeling task. To address this, we present a stochastic energy/reserve mixed integer linear program for a community energy system with consideration of local network constraints. By covering all the relevant energy vectors, the multi-energy formulation allows comprehensive modeling of different flexibility options, namely, multi-energy storage, energy vector substitution, end-service curtailment, and power factor manipulation. A key feature of the approach is its robustness against any reserve call, ensuring that occupant thermal comfort cannot be degraded beyond agreed limits in the event of a call. The approach is demonstrated through a case study that illustrates how the different flexibility options can be used to integrate more electric heat pumps into a capacity constrained smart district that is managed as a community energy system, while maximizing its revenues from multiple markets/services.

Techno-economic assessment of distribution network reliability services from microgrids

This paper proposes a new techno-economic framework for the optimisation of Microgrid (MG) operation considering energy and reserve services, as well as a novel distribution network reliability service. The price signals required to incentivise reliability services are formulated based on the potential of MGs to improve reliability levels. This potential is quantified based on sequential Monte Carlo simulations and economic values assigned to reliability according to existing UK regulation. The results, based on pragmatic MG data and a real distribution network, highlight strong synergies between reliability services, and energy and reserve services. These synergies allow MGs to provide distribution network reliability support without significantly changing their normal behaviour or compromising their capabilities to provide other services.

EHP in low voltage networks: Understanding the effects of heat emitters and room temperatures

The adoption of Electric Heat Pumps (EHPs) at residential level can support the decarbonization of domestic heating. However, these new loads can produce technical problems in low voltage (LV) distribution networks. This work studies the EHP technical impacts in LV feeders, analyzing potential actions to minimize them. Particularly, two cases are investigated: the utilization of different heat emitters and the selection of different indoor temperatures. These cases are explored by using a Monte Carlo approach to cater for the uncertainty of LV demand. Daily one-minute resolution profiles are adopted for the domestic energy consumption in each of the sensitivities considered. The methodology is applied on three real UK LV networks. Results show that the decrease in the set temperature leads to a small reduction in the likelihood of technical impacts, whereas the utilization of underfloor heating can significantly reduce the likelihood of such impacts, resulting in a higher EHP penetration.

FV-battery community energy systems: Economic, energy independence and network deferral analysis

This paper presents a mixed integer linear program based model to optimize smart community energy systems, based upon physical models of buildings, energy storage, and energy conversion devices. Using a level-of-aggregation approach, the model can accommodate different price components at the relevant aggregation level. Community energy systems with various characteristics have been assessed considering different objectives (i.e., energy minimization, cost minimization and electricity self-sufficiency). Community behavior and economic performance are assessed to demonstrate the impact of different operation objectives and energy storage options on network constraints and economics.

Exploiting electric heat pump flexibility for renewable generation matching

With the increasing penetration of renewable energy sources in the modern electric grid, it becomes more technically difficult and costly for system operators to balance generation and demand as traditional providers of flexibility (i.e., flexible generation) become uneconomic. Therefore new sources of flexibility are needed to maintain reliable operation. Flexible demand, including from electric heat pump (EHP) resources, is one source of flexibility which can be utilised to cope with the uncertainty of renewable generation by providing demand response services. In this paper, a high resolution and granular domestic energy consumption model is applied, which uses a four-node electrical analogue to represent the thermal characteristics of domestic dwellings. Then the performance of an EHP cluster coupled with dwellings is simulated. A control algorithm is designed to match the clusters electric load with renewable generation profile. Recognising the potentially detrimental effect of EHP flexibility exploitation on end-user thermal comfort, the loss of comfort level of occupants is assessed. The possibility of significant thermal discomfort from renewable generation matching is demonstrated.