Assessment and simulation of the responsive demand potential in end user facilities: application to a university customer

Abstract

Many problems have arose along with the practical implementation of restructured electrical business in USA and European Union such as lack of generation, network constraints, etc. A good example for these problems is the scarce participation of the demand in the electricity markets-energy, reserve and other ancillary services-problem that can be solved through new Demand Responsive Programs, aimed to replace the traditional Demand Side Management programs into voluntary demand participation programs. A methodology for the generation of demand side bids and offers in large customer facilities and a real application to a university customer is presented in this paper. This methodology is based on knowledge of the physical processes involved in the electricity consumption and the flexibility in the required supply. The result of the methodology proposed is a set of Demand Packages, that can be used to participate in different electricity markets, whose possibilities-in the market arena-will be explored in a consequent paper.

Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

“…This approach was proposed by Ihara and Schweppe [17]. A more detailed information about formulation and the physical meaning of the parameters of this kind of model can be found in [20,21,28,40] with simulation examples, coefficient values and data for EH, HVAC and WH loads. Summarizing, these models apply physical laws on loads and their environment (e.g., heat gains, heat losses, heat generation, heat storage, water or process flows), to determine the load behaviour according to the change of inputs in the system (in our case, electricity supply).…”

“…For instance, in the same period, 2016, PJM enrolled 2560 MW in Economic Programs (but only 146 MW were used [22]). If the customer is fully active and [20,40].…”

“…As mentioned previously, the aggregation process involves selecting individual loads with similar weather characteristics (the so called quasi-homogeneous control group [20]). Right now, the first alternative for the aggregation problem is to solve the elemental model for each load in the selected group and obtain an average demand (and service characteristics of such loads, i.e., internal temperature for space heating, or water temperature for water heaters) of the aggregated load (for example through Monte Carlo simulation).…”

“…Different proposals for aggregation have been proposed by Callaway [23], Perfumo [24], Mathieu [25], Alvarez [20] and Gomes [21]. The diversity in the approaches is related not only to the aggregation processes but to the modelling approach selected to characterize elemental loads.…”

“…The diversity in the approaches is related not only to the aggregation processes but to the modelling approach selected to characterize elemental loads. With respect to elemental models, the main difference is the physical phenomena being considered: [24][25][26][27] follow a model that only reproduce a simplified and inaccurate heat balance (heat losses/gains due to temperature gradient and electrical power conversion) or discard important inputs (solar radiation), whereas [20] and specially [21] take into account the main changes in sensible and latent heat that affects the behaviour of the load: i.e., solar radiation, internal sources (inhabitants, appliances), the losses/gains from other dwellings, the renewal of indoor air, or bidirectional heat fluxes [28]. The problem is that solar radiation through windows and walls are not a negligible factor in the modelling process of TCL.…”