# Incentive Price-Based Demand Response in Active Distribution Grids
## 1 Introduction
There are several possibilities for DSOs to engage the customers in DR programs such as executing a contract agreement with fixed payment for the committed power capacity, fixed incentive price for each kW of flexible power, time-of-use price for power consumption, time-varying electricity prices etc. [8]. Each of the above schemes have their own merits and demerits [9], and it is challenging for DSOs to choose the DR scheme that resolves the grid issues and at the same time does not incur higher costs. Deployment of smart meters at customer connection points has paved the way for collecting and analyzing the power consumption pattern at a higher time resolution. The historical power consumption data could be used for forecasting the power demand at future time intervals. Using meteorological data, PV power production could be predicted for the next few hours. Using the forecasts of the power consumption and generation, DSOs could run a load flow analysis and predict the grid voltage behavior in advance. If any voltage violations are expected, DSOs could trigger an incentive price signal thereby motivating the customers to increase/decrease their consumption. The advantage of the above method is the DR incentive price signal is generated only if it is deemed necessary. However, computing the optimum incentive price for the required voltage regulation is not straightforward and is the subject of the research work done in this paper. A comprehensive review of demand response strategies for the integration of more RES and improvement of grid efficiency can be seen in [9; 10; 11] and the references therein. From the control theoretical perspective, a DR scheme can use different methods such as centralized, decentralized, hierarchical distributed or purely distributed control method [12]. This paper focuses on centralized and hierarchical distributed control methods, as they are more suitable for control of distribution grids. The reason is that the DSOs and customers with DERs who are the two main actors have different control objectives and these two methods allow an easily formulation and computation of solutions that meet those objectives. The centralized method involves a central controller called low-voltage grid controller (LVGC) in this paper which is operated by the DSO to control all DERs in their LV grid [13]. In hierarchical distributed control method, the LVGC acts as a coordinator and communicates with the DER controllers to compute the optimum setpoints of DERs. A centralized DR control is typically applicable for grids under normal conditions in which DSOs want to utilize DERs to increase the grid energy efficiency. The centralized control method is not scalable, hence it is not feasible for large distribution grids with thousands of DERs. The centralized method is also not reliable due to its dependability on a single central controller. The above demerits are overcome in the distributed control method as this method involves cooperation among controllers and the failure of few controllers will not cause a large disruption. The hierarchical distributed control can be used for addressing the DSO's objectives such as to mitigate the over-voltages and for meeting the customers' requirement to minimize their electricity bills [14; 15]. The focus of this paper is to apply a novel DR method for two objectives, which are voltage regulation of LV grids and maximization of the self-power consumption of customers (thereby minimizing their electricity bills) by optimum utilization of their battery storage systems. Distributed model predictive control (DMPC) method can be employed for realizing a distributed DR scheme as discussed in [15] to make use of the forecasted power demand for calculating the optimum control setpoints to DERs. This is the motivation for using a DMPC method for the distributed implementation of the DR scheme in this paper. In order to dynamically compute the incentive prices that can solve the voltage regulation issues price-based distributed optimization methods [16; 17; 18] are to be considered.