Lingxi Zhang

Unified Unit Commitment Formulation and Fast Multi-Service LP Model for Flexibility Evaluation in Sustainable Power Systems

Classical unit commitment (UC) algorithms may be extremely time-consuming when applied to large systems and for long-term simulations (for instance, a year) and may not consider all the features required for flexibility assessment, including analysis of different reserve types. In this light, this paper presents a novel flexibility-oriented unified formulation of a large-scale scheduling model considering multiple types of plants (including storage) and reserves, which can seamlessly model binary (BUC), mixed integer linear programming (MILP), and relaxed linear programming (LP) UC. Comparisons are carried out on several case studies for a reduced model of Great Britain, assessing loss of accuracy (as measured according to various metrics specifically introduced) against computational benefits in different renewables scenarios with more or less flexible systems. It is demonstrated how the computational time of the LP model is significantly less than the BUC and MILP approaches while capturing with relatively high precision all the relevant flexibility requirements and allocation of multiple types of reserves to different types of plants. The results indicate that the proposed fast LP model could be suitable for various computationally intensive flexibility studies (e.g., Monte Carlo simulations or planning), with significant reduction in simulation time and only minor errors relative to established MILP models.

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.

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.

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.

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.

System level assessment of PV and energy storage: Application to the Great Britain power system

The main aim of this work is to add to the debate about the potential roles of photovoltaics (PV) and storage in the future GB power system. To do so, this paper has developed models to assess the system level performance of coordinated PV and storage in terms of the provision of system capacity and operational implications. More specifically, on the one hand, in the days with system critical peak demands, a centralised control strategy is considered to use storage to shift the energy generated by PV to supply system demand at peak times; on the other hand, in the rest of the year, storage is then controlled in a distributed way to primarily maximise the self-consumption of solar energy and secondly minimise the instantaneous feed-in power from PV panels. Afterwards, the contribution to system capacity from coordinated PV and storage is evaluated by using a capacity credit assessment, while the operational implications are analysed by focusing on the overall system operational cost and the amount of PV generation that needs to be curtailed. In summary, according to the assessments, it has been found that in the case of GB, the proposed coordinated control of PV and storage can, but to a limited extent, contribute to the provision of system capacity, reduce system operational cost, and increase the utilisation of solar energy.