Demand response with functional buildings using simplified process models

Abstract

Smart Grids ideally interconnect intelligent grid members. One big share of grid presence is with buildings. Flexible and grid-friendly buildings would improve grid management and are an important contribution to the integration of renewable energy sources. Classical buildings, however, are passive and not cooperative. This article describes how electro-thermal processes in buildings can be used for demand response and how such intelligent behavior can be enabled via communication technology. Experiments and simulations on typical mid-European buildings were done to estimate the potential time constants. I. MOTIVATIONBuildings are responsible for around forty percent of an economy's energy consumption in western countries [1]. To reach the given emission goals an obvious way is to increase the building's energy efficiency. Typical measures are improved building shell (insulation) or more efficient HVAC (heating, ventilation and air conditioning) equipment. Such efficiency measures immediately and directly reduce the overall energy demand and its related emissions. The typical development -especially observable in the European Union -is to increase efficiency to the maximum (a.k.a. Passive House Level) and in addition to add local renewable energy generation (e.g. photovoltaics, solar thermal, wind, ground water) to cover the remaining energy demand or to even reach "plus-energy" level. In the latter case, the building would generate a positive net energy balance, i.e. generate more than it needs. This positive net energy balance is not to be confused with energy autonomy, since the building is still grid-connected (electric, thermal) and dynamically changes between its roles as energy producer and energy consumer. Local, as well as centralized, renewable energy often has a stochastic behavior: it feeds in whenever the primary resource (solar radiation, wind) is available. By the use of a good prognosis, flexible conventional generation (gas turbine power stations) and potential storage (pump storage power plants) it is possible to keep these fluctuations under control. However, the increasing share of renewable generation and the approaching wave of electric vehicles with its thirst for electricity pose new challenges for our energy system and its generation, transport and distribution capacity. Intelligent buildings can help. "Intelligence" in this context means that the building is equipped with an advanced building automation system (BAS) that • influences energy-relevant equipment and settings like HVAC or windows, • senses energy-relevant information like occupancy, weather, or usage, and that • contains advanced control algorithms that go beyond plain PID (proportional, integral and derivative control function). Such an advanced BAS can use weather forecasts, learn usage profiles or reschedule the operations of building systems in order to meet smart grid requirements (see [2] and [3]). One key ingredient for operating in such a smart way is to know the dynamic behavior of the building. Model-based control ...

A Survey on Demand Response Programs in Smart Grids: Pricing Methods and Optimization Algorithms

“…For example, in [65] and [66] an aggregator is used in order to derive scheduling decisions. Centralized management of loads is an effective solution for controlling buildings ( [67], [68]) and charging stations for PHEVs [69]- [72]. For instance, a centralized method for PHEV charging is proposed in [69] that schedules PHEV charging times based on weights, which define critical and non-critical demand periods.…”

Demand side management for city districts

“…In purely centralized approaches, a central coordinator makes all decisions and the lower hierarchies, such as buildings and their heating devices, execute these decisions [42,43]. Centralized approaches provide the theoretically best coordination results, however the capability limits are quickly reached to support complex cases such as the integration of renewable energies due to exhaustive data management and robustness issues.…”

New real‐time demand‐side management approach for energy management systems

“…To demonstrate the effectiveness of the proposed DSM approach, it is incorporated in a centralised EMS of an islanded MG. In centralised control, the central controller is responsible for all decision making as in [29][30][31][32]. This scheme increases the system security [33].…”