Data Model for Residential and Commercial Buildings. Load Flexibility Assessment in Smart Cities

Abstract

Demand response (DR) programs were usually designed to provide load peak reduction and flatten the load curve, but in the context of rapid adoption of emerging technologies, such as smart metering and sensors, load flexibility will address current trends and challenges (such as grid modernization, demand, and renewables growth) encountered by the evolving power systems. The uncertainty of the renewable energy sources (RES) and electric vehicle (EV) fleet operation has increased the importance of load flexibility that can be managed to provide more support for the stable operation of power systems, including balancing. In this paper, we propose a data model to handle load flexibility and take advantage of its benefits. We also develop a methodology to collect and organize data, combining the consumption profile with several auxiliary datasets such as climate characteristics of the location, independent system operator (ISO) to which the consumer is affiliated, geographical coordinates, assessed flexibility coefficients, tariff rates, weather forecast for day-ahead flexibility forecast, DR-enabling technology costs, and DR programs. These multiple features are stored into a flexibility relational database and NoSQL database for large consumption data collections. Then, we propose a data processing flow to obtain valuable insights from numerous .csv files and an algorithm to assess the load flexibility using large residential and commercial profile datasets from the USA, estimating plausible values of the flexibility provided by two categories of consumers.

The potential of energy planning at Municipality scale: Sustainable Energy and Climate Action Plans (SECAP) and local Energy Communities to meet the energy demand variability

“…The hourly thermal load for residential and tertiary sectors of the Municipality has been identified starting from a reference normalized trend [19] (Figure 4) expressed in kW/kWh referred to the whole Municipality, and valid during the autumn and winter months. The trend was built starting from predicted daily heat demand profiles [19] , normalized by the total daily consumption for one selected building per building category and with constant outdoor temperature equal to 0°C.…”

“…The hourly thermal load for residential and tertiary sectors of the Municipality has been identified starting from a reference normalized trend [19] (Figure 4) expressed in kW/kWh referred to the whole Municipality, and valid during the autumn and winter months. The trend was built starting from predicted daily heat demand profiles [19] , normalized by the total daily consumption for one selected building per building category and with constant outdoor temperature equal to 0°C. Considering also the annual number of hours of heating turning on defined by the Italian regulation [20], and the average hourly energy demand of the Municipality calculated from the results of SECAP [5] (Table 2), the curves in Figure 4 can be adapted to specific need of the Municipality considered.…”

Upscaling Energy Efficiency via Energy Communities

“…The total consumption of the building was transformed into an hourly load profile based on (Mihaylov et al, 2018) and (Oprea et al, 2021). Using PV SOL software, 7 variants of PV power plants were created -2 horizontal systems (Var1 and Var2) and 5 mounted systems with an angle of 15° (Var 3 to Var 7).…”

Intelligent System Application to Monitor the Smart City Building Lighting

“…In this regard, the main building blocks (structures) for a city to become a smart city will be the gradual advancement of technology [15], as well as paving the way for optimizing energy consumption and management. When implementing and managing smart cities, numerous factors must be carefully considered [16]. Calvillo et al [17] investigated all aspects of energy in the smart city and its communications, and they present a number of existing models and simulation tools.…”