Читать книгу Mathematical Programming for Power Systems Operation - Alejandro Garcés Ruiz - Страница 18

Demand-side management constitutes a paradigm shift in the context of smart grids, where loads are active components subject to optimization. The role of demand-side management is crucial to decrease CO₂ emissions, reduce the bottleneck in the transmission system, diminish operational cost, and improve efficiency. In order to attain these objectives, it is required to apply mechanisms of electrical load management with static and dynamic techniques. Static techniques involve administrative measures as policies and activities to incentive the end-users to change their energy demand pattern; dynamic techniques include actions to reduce the electricity consumption, such as peak-clipping, valley filling, and load shifting, among others. Information and communication technologies (ICT), as well as the concept of the Internet of the Things (IoT), allow to control the loads and integrate this control in a centralized optimization model.

In general, controllable loads are introduced in a model very similar to the economic dispatch. Its basic structure is presented below:

(1.5)

where p¯it is the power required by the load i at time t; pit is the amount of power that is reduced due to the demand-side management model; cit is the cost of disconnecting one unit of power; and dt is the minimum demand. This is only the basic optimization model, which can be modified, in order to include more type of loads and other aspects of the operation of the system.

Some loads can be moved in time, for example, the washing machine in a residential user. These loads, known as shifting loads, can be optimized by defining the load’s optimal starting time. This optimization model is binary but tractable as presented in Chapter 13.

A demand-side management model can also include a model for tertiary control in microgrids or a model for charging electric vehicles. The latter is usually called vehicle-to-grid or V2G. In these cases, the optimization model requires to be executed in real-time by an aggregator as depicted in Figure 1.4.

Figure 1.4 Vehicle-to-grid concept with an aggregator that centralizes control actions. Dashed lines represent a communication architecture with the aggregator.

An aggregator is a crucial component in modern smart distribution networks. This device receives information of the final users – in this case, the electric vehicles – and gives the control actions in order to obtain a smart operation. However, the intelligent part of this system is not in the hardware but in the optimization required to solve the problem efficiently and in real-time; therein lies the importance of understanding the optimization model.

A V2G strategy can be unidirectional or bidirectional. In unidirectional V2G, an aggregator controls the electric vehicles’ charge similarly as shifting loads. In bidirectional, the electric vehicle can inject power into the grid if required for improving the operation. In any case, the model can become stochastic since the state of charge of the vehicles can be unknown, and the aggregator does not control the arrival/departing time of the vehicles¹. The aggregator can also incorporate economic dispatch and OPF models to manage other distributed resources such as local batteries, solar panels, and wind turbines. Chapter 11 examines these problems.