Optimization under uncertainty of thermal storage-based flexible demand response with quantification of residential users' discomfort

Abstract

This paper presents a two-stage stochastic programming model for provision of flexible demand response (DR) based on thermal energy storage in the form of hot water storage and/or storage in building material. Aggregated residential electro-thermal technologies (ETT), such as electric heat pumps and (micro-) combined heat and power, are modeled in a unified, non-technology specific way. Day-ahead optimization is carried out considering uncertainty in outdoor temperature, electricity and hot water consumption, dwelling occupancy, and imbalance prices. Building flexibility is exploited through 1) specification of a deadband around the set temperature, or 2) a price of thermal discomfort applied to deviations from the set temperature. A new expected thermal discomfort (ETD) metric is defined to quantify user discomfort. The efficacy of exploiting the flexibility of various residential ETT following the two approaches is analyzed. The utilization of the ETD metric to facilitate quantification of the expected total (energy and thermal discomfort) cost is also demonstrated. Such quantification may be useful in the determination of DR contracts set up by energy service companies. Case studies for a UK residential users' aggregation exemplify the model proposed and quantify possible cost reductions that are achievable under different flexibility scenarios.

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

“…Here there are also gaps to be found, as those works which do consider multi-energy optimization, [13]- [15], do not consider reserves from electro-thermal devices. Given the identified gaps in the literature, this work proposes a two-stage (day-ahead -DA -and real-time) stochastic energy/capacity co-optimization model, building on the energy optimization model proposed in [16]. This model may be employed as a self-scheduling tool for an aggregator (or similar party) with the responsibility to optimize a smart district, or it may be used by any interested party to understand the value of various business cases for one or more districts (or VPPs).…”

Stochastic model predictive control for optimal energy management of district heating power plants

“…We calculate estimates of upper and lower bounds on the optimal value of the original stochastic problem (18), and of the optimality gap, by solving many SAA instances for different sample sizes and using Latin Hypercube sampling (LHS) as sampling technique. LHS has been proved to compute an unbiased estimator with considerably less variance than the one obtained from Monte Carlo sampling technique (see [22] and references therein for details on the LHS method).…”

“…In [17] a two-stage stochastic MILP model is designed to determine the optimal number and size of CHP system components. The authors in [18] propose a two-stage stochastic programming framework to optimize the use of a thermal energy storage in the form of hot water storage and/or storage in building material. Three outside temperature scenarios and ten price scenarios are combined into thirty scenarios; then, occupied/non-occupied scenarios are randomly assigned.…”

Techno-economic assessment of community energy solutions to network capacity issues

“…In this regard, development of a multienergy view may be particularly useful [8]. A key insight such a view brings is the flexibility available from utilizing storage in an intermediate energy vector, such as hot water in a domestic hot water (DHW) system or hydronic heating system [9], or in the building fabric, considering heating or cooling. This may be especially relevant given the substantial amount of energy storage available in heating systems and building fabric.…”

“…Addressing the issues described above, and drawing on previous work [9], [13], this paper describes a district or community energy optimization model, considering space heating/cooling, DHW and electricity demand, and generation from solar PV. Case studies are presented to demonstrate how the model can be used to assess the capability of districts with energy storage to ameliorate capacity constraints, and how that flexibility can be further exploited for economic gain given access to varying electricity-related prices (for electricity itself, UoS fees and taxes).…”