# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 4 Discussion
### Applications of Flexibility Quantification
#### 4.1.1 Integration of Renewable Energy Resources  
Renewable energy technologies are reliant on energy resources, such as wind and solar irradiance, which are intermittent and somewhat stochastic [1]. Although there has recently been significant progress in developing more accurate predictive models for these resources, forecast uncertainties can still introduce challenges for power grid operation and security [70]. This is especially problematic at high penetration levels of renewable resources. Traditionally, one solution has been to over-design wind and solar power plants to allow for some level of operational flexibility when the available energy is less than expected or desired. In fact, early efforts in replacing fossil fuel-based power plants with renewable counterparts relied on increasing the new plant's rated capacity compared to the decommissioned one in order to compensate for any drop in available generation. With the drop in price of utility-scale batteries in conjunction with more efficient chemistries, the focus has shifted toward combining renewable resources with energy storage [71]. While both approaches are effective in turning renewable generation into a dispatchable resource, they incur additional costs and can lead to negative environmental impacts. Demand flexibility is a relatively inexpensive, environmentally friendly, and readily available resource that can be combined with renewable generation to reduce its operational uncertainties [72]. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Author** & **Methods** & **Case Study** & **Granularity** & **End-Uses** & **Metric(s)** & **Data Inputs** & **Sector** \\ \hline Kohlhepp and Hagenmeyer [60] & Thermal model of buildings & X & Aggregate & HVAC, heat pumps, cold storage & Storage capacity (0) & Technology characteristics, building stock & R, C \\ [PERSON] et al. [61] & Flexibility models for building types & & Single & TCLs & Shifted energy amount (kWh) Cost savings (S), peak demand reduction (W/\(f^{2}\)), percentage weight (\(\%\)) & Demand, SOC & I \\ [PERSON] and [PERSON] [62] & Simulation, optimization (indoor air temperature) & Single & AC, lighting & AC, lighting & demand reduction (\(W/f^{2}\)), percentage weight (\(\%\)) & Building characteristics, weather, temporal electricity prices & [PERSON] \\ [PERSON] [63] & Clustering & X & Aggregate and AC & EV, clothes washer and deer, dishwasher, and AC & Demand reduction & Demand reduction (MWh) & Demand reduction & R \\ [PERSON] [64] & Optimization & Aggregate & PV, EV, EVSE & Load shift potential (GW) & Mobility data, grid operations & R, [PERSON] \\ [PERSON] et al. [65] & Simulation, flexibility and NPV model of batteries & X & Aggregate & Battery, PV & Peak shaving potential (\(\%\)) & Demand, temporal electricity prices & R \\  & & & & Maximum power shedding point, maximum power maximum power & electricity prices & \\ [PERSON] et al. [66] & Simulation, neural network, Bayesian framework & X & Single & AC & Secondary shedding, mean power shedding, mean power rebound, maximum & Weather, internal disturbances, demand DR rebound & [PERSON] \\ [PERSON] and [PERSON] [67] & Optimization & X & Single & Manufacturing workstations & DR potential (kW) & Demand, factory layout, crew availability & I \\ [PERSON] and [PERSON] [68] & Simulation, flexibility model of building & Aggregate & & Emissions reduction (\(\%\)) & Grid emissions, demand, grid operations, benchmarking data & [PERSON] \\ [PERSON] and [PERSON] [69] & Export and import flexibility models & Aggregate & Wind, dishwasher, clothes washer & Maximum addressable opportunity (\(\%\)) & Maximum addressable characteristics, participation rates & R, C \\ \hline \hline \end{tabular}
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Table 2: _Cont._Quantifying the availability of demand-side flexibility can assist electric utilities in multiple ways.