Demand Response / Load Management

Demand Response or Load Management is a feature which is closely connected to DER (Clause 4.3.6), AMI (Clause 4.3.7) and HBES/BACS (Clause 4.3.9). Many of the standards and descriptions have already been addressed there.

To comply with the ambiguous goals of climate policies, in the future renewable energy resources will have a larger significance. Compared to the easy plan and adjustable power generation with fossil and nuclear fuel, renewable power generation is only in parts plan- and adjustable (e.g. solar, wind) or is subject to other restrictions (hydro). This means that in the future the share of “easily” adjustable power generation will decrease, which poses new challenges to a future energy management system.

One approach to the solution of this problem is the paradigm shift from “generation follows load” to “load adapts to generation”. Therefore load management will have a much higher significance in future. Load management has been performed in the past, e.g. large and small consumers (e.g. night storage heater). However it was limited to the prevention of peak loads and the respective load shedding of day and night load curves. These solutions, however, had only a limited influence on the control of individual loads.

Demand response (DR) is similar to dynamic demand mechanisms to manage customer consumption of electricity in response to supply conditions, for example, having electricity customers reduce their consumption at critical times or in response to market prices. The difference is that demand response mechanisms respond to explicit requests to shut off, whereas dynamic demand devices passively shut off when stress in the grid is sensed.

Demand response is generally used to refer to mechanisms used to encourage consumers to reduce demand, thereby reducing the peak demand for electricity.

Load management / Demand Response can be performed in two respects:

• Energy management: this means the energy balance needs to be achieved in each charging period, generally 15 to 60 minutes.

• Near real-time power management: this means energy needs to be balanced at all

The latter poses significantly higher requirements to the control speed and can be realized only through fully automated, closed control loops. In all cases an integration of the consumer in the power grid automation requires a seamless communication. Area-wide smart meter utilization will be a major contributor to such a development. Load management and demand response solutions can be realized through an interface to control individual loads within the consumer premises.

An incentive can be set by a price signal, which is transmitted to the consumer, e.g. a real time price signal. The consumer then still has the choice of whether he will change his own power consumption according to the set price incentives. In this case it is not important whether such a decision is taken by the consumer himself or an intelligent control system.

The behaviour of such systems is not easily predictable, no matter which of the above systems is in place. Therefore these systems cannot support a real fast energy balancing and are therefore only capable of supporting energy management. Another problem with incentives is the choice of the optimal incentive. Normally incentive programmes will follow monetary considerations. However since electrical energy is a basic necessity this will pose a conflict between social fairness and a sufficiently high price difference between times of high and low power availability.

Therefore incentives will not be sufficient in the long run and must be extended to direct intervening control mechanism. An integration of power grid automation and building and home automation offers the possibility to make full use of the flexibility and energy storage option of consumers for power grid balancing.

This power grid balancing requires load models for the optimizing software, in order to be able to predict the load behavior of the overall system. These load profiles describe the limits of time flexibility of consumers and their energy storage potential. Only with this information available can a predictive load management be realized, which avoids a decreasing quality of energy supply for the consumer.

As with virtual power plants, buildings can then be summarized in an electrical energy sense and control the energy supply in a way which satisfies all load demands and at the same time act as part of the overall energy system. Decentralized energy resources can be embedded in such a system.

In such a system there is a planning phase and real-time operation control. In the planning phase, the energy consumption or generation is predicted for e.g. the next day and energy transfer is optimized to the condition of the overall energy market. From this the optimal offering to the overall energy market can be derived.

Figure 16 – Control principles of a virtual power plant

A superior power grid control level (see Figure 16) can use the power offered by the virtual power plant to optimize power balance in the overall system. Such a hierarchically organized system can integrate demand response in existing grid structures without the need to reorganize existing power installations. Virtual power plants offer the solution of integrating building and home automation in the power grid.

Core elements of a Demand Response application will be the distribution management system, smart metering systems and building automation.

4.3.8.2 Requirements

The main requirement for Demand Response is the active involvement of the consumer, which must be achieved through a transparent pricing mechanism. Furthermore information concerning current load and generation, a forecast of these quantities and a real-time measurement are requirements for Demand Response.

The availability of equipment for manageable loads (electricity heating, ventilation, smart appliances, e-cars, etc.), generation (DER, bulk wind and solar power, etc.) and storage (distributed like e-cars or bulk storage) is a prerequisite for Demand Response. The information exchange and control of these systems require an information exchange across several domains, e.g. from bulk generation down to smart appliances. A building operator will have significant influence on the choice of manageable loads, sources and storage which will be controlled within the building itself (and therefore be controlled through the Building Automation) and which loads, sources and storages will be directly controlled by the power grid.

Data models and protocols must be available across all levels.

Connecting conditions must be standardized, in order to allow a dynamic configuration of the overall system.

Furthermore security and data security are important. Failure to achieve security of the infrastructure is less severe than in the case of the transmission systems. However privacy issues may play an important role, since there are various local regulations and laws which need to be accommodated.

4.3.8.3 Existing Standards Power grid

IEC 61968, Application integration at electric utilities - System interfaces for distribution management

IEC 61850-420, Communication networks and systems for power utility automation - Part 7-420: Basic communication structure - Distributed energy resources logical nodes

Building

ISO 16484 series, Building automation and control systems (BACS)

ISO/IEC 14543-3, Information technology -- Home Electronic System (HES) architecture EN 13321 series, Open data communication in building automation, controls and building management - Home and building electronic systems

EN 50090 series, Home and building electronic systems (HBES)

EN 50428, Switches for household and similar fixed electrical installations - Collateral standard - Switches and related accessories for use in home and building electronic systems (HBES)

EN 50491 series, General requirements for Home and Building Electronic Systems (HBES) and Building Automation and Control Systems (BACS)

China: GB/Z 20965, Information technology -- Home Electronic System (HES) architecture USA: ANSI/ASHRAE 135, BACnet - A Data Communication Protocol for Building Automation and Control Networks

4.3.8.4 Gaps

Profiles between Power Automation, Building Automation and Metering are missing.

4.3.8.5 Recommendation Recommendation S-DR-1

The Distributed Energy Management System (DEMS) and the Building Automation System (HBES/BACS) must be brought together at the domain interface. A set of profiles should be described and standardized in order to give guidelines for paths to the interoperability of these two domains. This task should be performed jointly in liaison with IEC TC 13, TC 57, ISO TC 205 and ISO/IEC JTC 1.

4.3.9 Smart Home and Building Automation